Book References

A detailed concept index across my AI/ML book collection — which books cover which topic, and on which pages. Reference only; the source PDFs live in my private Drive.

Note📚 About this library

A cross-index of my AI/ML book collection: for each topic, the books that cover it and the page numbers where. Search by topic or book below. Reference only — the PDFs are in my private Drive, so there are no links.

Vectors & Vector Operations (62 books)

Book	Author	Pages / Concepts
12.LAEF	—	Abstract Vector Spaces; Vector Space Axioms; A Gallery of Vector Spaces (+1)
13.Machine-Learning-Systems	—	929–931, 1371
5.math4ml	Deisenroth, Faisal & Ong	6, 10, 26, 43
6 390 lecture notes spring24	—	71–72, 114–116
7.pen and paper exercise in ML	—	24–26
8.matrixcookbook	Petersen & Pedersen	10–11, 14–16, 61
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~15–24
An Introduction to Machine Learning - Machine Learning Summer	—	L4: Support Vector estimation; L5: Support Vector estimation
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.4 Support Vector Machine
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	116–120, 125
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	77–78
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	763–766, 1074–1075
Basics of Linear Algebra for Machine Learning	Jason Brownlee	60–66, 69–71, 82, 196
Essential Math for AI	Hala Nelson	Notation: Vectors in this book are alway; Support Vector Machines; Action of A on the Right Singular Vector (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	22–23, 58–60, 120, 199–200
Financial Signal Processing and Machine Learning	—	346–362
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	76
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	263–267
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	141–145, 149
grokking-deep-learning	—	~97, ~212, ~215–216, ~219
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	487–488
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	225, 715–716
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	203
Introduction to Artificial Intelligence	—	287–288, 299
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	32–35, 39–40, 47, 84–87, 191–192
Introduction to Machine Learning with Applications in Information Securit	—	114–120, 134, 221
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	78–79, 119, 169–171, 267–268
Language Models Interview Handbook	—	28, 34, 64, 106, 122
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	200–203
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	65–66
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	145
Machine Learning for Hackers	Conway & White	291–299
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	291–299
Machine learning in action	Peter Harrington	60–61, 94–97, 128, 345–348
Machine Learning in Action	Peter Harrington	60–61, 94–97, 128, 345–348
Machine Learning in Healthcare Informatics	—	46, 194, 244
Machine Learning in Python	—	163–164
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~221–228, ~243–246
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	123–124, 128–129, 177–178, 287–288, 348–349, 353
Machine Learning with TensorFlow	—	~210–211
Machine Learning: An Algorithmic Perspective, Second Edition	—	~169, ~179–183, ~291–299
Machine Learning: Hands-On for Developers and Technical Professionals	—	165–167, 170–173, 344
Machine-Learning-Systems	—	911–913, 1351
MachineLearningNotes	—	33, 150, 170, 174–176
Master Machine Learning Algorithms - Discover how they work	—	~106–124
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	198
Mastering Machine Learning with scikit-learn 2nd edition	—	176, 181–183
mml-book [Reading]	—	41–45, 78–80, 86, 145–146, 155–160, 376–377, 380–393
Neural Networks and Deep Learning: A Textbook	—	30, 83–84, 138–140, 465–467
Practical Linear Algebra for Data Science - Mike X Cohen	—	2. Vectors, Part 1; Creating and Visualizing Vectors in NumP; Geometry of Vectors (+1)
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	137–139
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	40–41
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	158–160
Principles And Theory For Data Mining And Machine Learning	—	277–279, 305–308, 359
Pro Machine Learning Algorithms	—	180–185
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	355
Python Machine Learning	—	190–216
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	276, 281, 287, 309
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	276
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	122
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Vectors and basic properties; Representing vector; Addition/subtraction of vectors (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	457–459

Matrices & Matrix Operations (52 books)

Book	Author	Pages / Concepts
10.marl	—	~321–322
12.LAEF	—	Special Matrices; Matrix Representations; Adjoints & Transposes (+1)
13.Machine-Learning-Systems	—	932–934
5.math4ml	Deisenroth, Faisal & Ong	8, 16–18, 22–23, 26–28
6 390 lecture notes spring24	—	113–118
7.pen and paper exercise in ML	—	17–18, 29–31, 34
8.matrixcookbook	Petersen & Pedersen	10–11, 14–16, 24–25, 46–57, 61
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~15–24
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	104–105, 108, 126–132, 185–186
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	96–99
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	141–142
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	1074–1075
Basics of Linear Algebra for Machine Learning	Jason Brownlee	25, 59, 74–84, 87–93, 96, 106–110, 125, 133, 136, 141–142, 159, 170, 197–199
Essential Math for AI	Hala Nelson	The One-Dimensional Case: Multiplication; The Two-Dimensional Case: Multiplication; Matrix Factorization (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	120, 198, 203–204
Financial Signal Processing and Machine Learning	—	157, 169–183
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	171–173, 201
grokking-deep-learning	—	~103–106, ~217–218
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	138–140
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	136–137
Introduction to Artificial Intelligence	—	166–167, 269–270
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	35–43, 46, 53–54, 58, 82
Introduction to Machine Learning with Applications in Information Securit	—	89–91
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	103–104, 314, 329
introduction-to-algorithms-and-machine-learning	—	101–108
Language Models Interview Handbook	—	108
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	219–222
Machine learning in action	Peter Harrington	310, 362–367
Machine Learning in Action	Peter Harrington	310, 362–367
Machine learning in bioinformatics	—	69–88
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	268, 339–341, 348–352, 362–364
Machine Learning with TensorFlow 1x	—	47–51
Machine Learning: Hands-On for Developers and Technical Professionals	—	345
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	39–40
Machine-Learning-Systems	—	914–916
MachineLearningNotes	—	31
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	147, 293, 302–303
Mastering Machine Learning with scikit-learn 2nd edition	—	232–233
mml-book [Reading]	—	28–32, 104, 135–140, 161–163
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	96–100, 133
Neural Networks and Deep Learning: A Textbook	—	90, 96–97, 115–119, 351–353
Practical Linear Algebra for Data Science - Mike X Cohen	—	Transpose; 5. Matrices, Part 1; Creating and Visualizing Matrices in Num (+1)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	40–41, 290–291, 477–481
Principles And Theory For Data Mining And Machine Learning	—	498
Pro Machine Learning Algorithms	—	23, 323–332
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	152–155, 390–395, 417
Real-World Machine Learning	—	112
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	224, 481, 486
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	44–47, 213, 332
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Introducing matrix; Augmented matrix; Basic matrix operations (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	62, 457–462
Why Machines Learn The Elegant Math Behind Modern AI - Anil Ananthaswamy [Reading]	—	Chapter 6: There's Magic in Them Matrice

Matrix Inverse & Determinant (16 books)

Book	Author	Pages / Concepts
12.LAEF	—	Inverse & Invertibility; The Pseudoinverse
5.math4ml	Deisenroth, Faisal & Ong	16, 25
7.pen and paper exercise in ML	—	17–18, 32–33
8.matrixcookbook	Petersen & Pedersen	6–8, 21–23
Basics of Linear Algebra for Machine Learning	Jason Brownlee	100, 143–144, 176–177
Essential Math for AI	Hala Nelson	The Pseudoinverse
introduction-to-algorithms-and-machine-learning	—	167–206
Machine learning in action	Peter Harrington	365
Machine Learning in Action	Peter Harrington	365
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	362–367
mml-book [Reading]	—	105–110
Neural Networks and Deep Learning: A Textbook	—	243
Practical Linear Algebra for Data Science - Mike X Cohen	—	Determinant; Computing the Determinant; Determinant with Linear Dependencies (+1)
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	416
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Invertible matrices; Properties of Matrix Inverse; Determinant (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	323–330

Eigenvalues & Eigenvectors (21 books)

Book	Author	Pages / Concepts
12.LAEF	—	Eigenvalues & Eigenvectors; Eigenvalue Complexities; Complex Eigenvalues & Oscillation (+1)
5.math4ml	Deisenroth, Faisal & Ong	15
7.pen and paper exercise in ML	—	15–18
8.matrixcookbook	Petersen & Pedersen	10–11, 30
Basics of Linear Algebra for Machine Learning	Jason Brownlee	132–135
Essential Math for AI	Hala Nelson	Singular Value Decomposition Versus the ; Computing an Eigenvector Numerically; Eigenvalue Density of the Sum of Two Lar
Financial Signal Processing and Machine Learning	—	111, 131–150
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	96–98
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	49–50
Introduction to Machine Learning with Applications in Information Securit	—	87–88, 296–298, 303
Language Models Interview Handbook	—	108
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	365
MachineLearningNotes	—	34–39
Mastering Machine Learning with scikit-learn 2nd edition	—	234–235
mml-book [Reading]	—	111–124, 339–340
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	129–152
Practical Linear Algebra for Data Science - Mike X Cohen	—	13. Eigendecomposition; Interpretations of Eigenvalues and Eigen; Finding Eigenvalues (+1)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	42
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	469–470
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	484
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Eigenvalues and vectors; Eigen properties; Existence of zero eigenvalue (+1)

Singular Value Decomposition (SVD) (20 books)

Book	Author	Pages / Concepts
12.LAEF	—	Singular Value Decomposition; Spheres, Ellipsoids, & Singular Values; Constructing the SVD (+1)
5.math4ml	Deisenroth, Faisal & Ong	20
8.matrixcookbook	Petersen & Pedersen	31
Basics of Linear Algebra for Machine Learning	Jason Brownlee	139, 176–177
Essential Math for AI	Hala Nelson	6. Singular Value Decomposition: Image P; Breaking Down the Circle-to-Ellipse Tran; The Ingredients of the Singular Value De (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	201–202
Introduction to Machine Learning with Applications in Information Securit	—	98
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	102, 123, 319–325
Machine learning in action	Peter Harrington	307–308, 311–312, 319–324
Machine Learning in Action	Peter Harrington	307–308, 311–312, 319–324
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	362–364
mml-book [Reading]	—	125–134
Neural Networks and Deep Learning: A Textbook	—	94–95
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	153–166
Practical Linear Algebra for Data Science - Mike X Cohen	—	14. Singular Value Decomposition; The Big Picture of the SVD; Singular Values and Matrix Rank (+1)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	43, 479–481
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	405–409, 413–415
Real-World Machine Learning	—	243–244
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	484
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Singular value decomposition

QR / LU / Cholesky Decomposition (9 books)

Book	Author	Pages / Concepts
12.LAEF	—	LU Decomposition; The QR Decomposition
8.matrixcookbook	Petersen & Pedersen	32
Basics of Linear Algebra for Machine Learning	Jason Brownlee	126–129, 174–175
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	202
mml-book [Reading]	—	120
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	69–86
Practical Linear Algebra for Data Science - Mike X Cohen	—	9. Orthogonal Matrices and QR Decomposit; QR Decomposition; 10. Row Reduction and LU Decomposition (+1)
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	483
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	LU decomposition; QR decomposition

Matrix Factorization (17 books)

Book	Author	Pages / Concepts
12.LAEF	—	Robust Matrix Factorization; Network Architecture & Matrix Factorizat
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	96–99
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	141–142
Basics of Linear Algebra for Machine Learning	Jason Brownlee	25, 125
Essential Math for AI	Hala Nelson	Matrix Factorization
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	103–104, 314, 329
Machine learning in action	Peter Harrington	310
Machine Learning in Action	Peter Harrington	310
Machine learning in bioinformatics	—	69–88
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	362–364
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	302–303
mml-book [Reading]	—	104
Neural Networks and Deep Learning: A Textbook	—	90, 96–97, 115–119
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	477–481
Pro Machine Learning Algorithms	—	323–332
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	417
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Matrix decomposition

Norms, Rank, Trace & Orthogonality (21 books)

Book	Author	Pages / Concepts
12.LAEF	—	Inner Products & Orthogonality; Angles & Orthogonality; Orthogonal & Orthonormal Bases (+1)
3.Reinforcement Learning- An Overview	Kevin Murphy	43
5.math4ml	Deisenroth, Faisal & Ong	12–16, 22–23
7.pen and paper exercise in ML	—	10–12
8.matrixcookbook	Petersen & Pedersen	14–16, 49, 61
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	44–45
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~192–204
Basics of Linear Algebra for Machine Learning	Jason Brownlee	69–71, 92–93
Essential Math for AI	Hala Nelson	Control the Size of the Weights by Penal; Penalizing the l 2 Norm Versus Penalizin
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	466–468
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	427
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	44–50, 53
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	75
Machine learning in action	Peter Harrington	366
Machine Learning in Action	Peter Harrington	366
Machine Learning: Hands-On for Developers and Technical Professionals	—	51
mml-book [Reading]	—	77, 82–96, 319–320
Neural Networks and Deep Learning: A Textbook	—	243
Practical Linear Algebra for Data Science - Mike X Cohen	—	Orthogonal Vector Decomposition; Matrix Norms; Matrix Trace and Frobenius Norm (+1)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	42
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Norm; Dot product and orthogonality; Orthogonal and orthonormal basis (+1)

Projection & Subspaces (14 books)

Book	Author	Pages / Concepts
12.LAEF	—	Subspaces; Span & Linear Independence; Orthogonal Subspaces & Complements (+1)
5.math4ml	Deisenroth, Faisal & Ong	7, 12–14
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~322–329
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	118, 199–200, 203–204
Financial Signal Processing and Machine Learning	—	111, 131–150
Handbook of Statistics: Machine Learning: Theory and Applications	—	~301–326
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	290, 317–319
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	247, 267–268, 280–281
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	35, 53
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	105–108
mml-book [Reading]	—	87–96, 319–320, 331–338
Practical Linear Algebra for Data Science - Mike X Cohen	—	Subspace and Span
Principles And Theory For Data Mining And Machine Learning	—	199–203, 521–523, 554–555
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Subspaces; Dimension of subspace; Subspaces of matrix and orthogonality (+1)

Tensors (17 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	559–560, 2275–2286
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~1–30, ~105–113, ~206–210
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	15–17
Basics of Linear Algebra for Machine Learning	Jason Brownlee	114–120
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python	—	57–69
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	58–69
Deep Learning. Practical Neural Networks with Java	—	223–227
grokking-deep-learning	—	~233, ~237–239
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	346–349, 355–356, 367–370, 399–401, 429–431, 435–436, 477–478, 504, 519, 532–533, 547–549, 582–583, 633–634, 647–648, 652–653, 724–727, 733–735
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	368–371, 431–437, 461–462, 469, 484, 503–504, 749–759, 794–797, 822–824, 829–830
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	209
Machine Learning with TensorFlow	—	~21, ~25–31, ~44–48, ~100–101, ~182–186
Machine Learning with TensorFlow 1x	—	21–22, 64, 69, 173–175, 211, 263–264, 267–273, 287–291
Machine-Learning-Systems	—	541–542
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	42, 127
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Tensors; Dot product of tensors; Tensor calculus (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	457–459

Derivatives & Differentiation (24 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	138, 142, 147, 160–163, 169, 189–190
13.Machine-Learning-Systems	—	511–529
6 390 lecture notes spring24	—	113–118
7.pen and paper exercise in ML	—	24–26, 29–33
8.matrixcookbook	Petersen & Pedersen	8–16, 24–26
Essential Math for AI	Hala Nelson	Derivatives of linear algebra expression
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	185–186, 191
grokking-deep-learning	—	~68–70, ~173
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	765–769
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	813
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	57
Language Models Interview Handbook	—	109
Machine Learning Algorithms with Applications in Finance	—	83–84, 91, 98–135
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	201
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	270–272, 354
Machine-Learning-Systems	—	493–511
MachineLearningNotes	—	31
mml-book [Reading]	—	147–154, 165–170
Neural Networks and Deep Learning: A Textbook	—	36, 163–164
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	277–278, 299–324
Principles And Theory For Data Mining And Machine Learning	—	498
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	88–92, 106–107, 111, 155, 161, 170–173, 178, 183–186
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Derivative of a function; Higher Order derivatives; Derivative of scalar fields w.r.t. vecto (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	111–113

Partial Derivatives & Chain Rule (7 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	144, 147, 166, 169
5.math4ml	Deisenroth, Faisal & Ong	28, 38
6 390 lecture notes spring24	—	116
Essential Math for AI	Hala Nelson	Chain Rule and Backpropagation: Calculat
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	57
Language Models Interview Handbook	—	110–111
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Directional derivative and partial deriv; Chain rule for derivatives of vector fie; Matrix form of the chain rule

Gradient, Jacobian & Hessian (28 books)

Book	Author	Pages / Concepts
10.marl	—	~169–174, ~195–214, ~230–241
13.Machine-Learning-Systems	—	212–215, 667–673
3.Reinforcement Learning- An Overview	Kevin Murphy	49, 54–55, 66, 107
5.math4ml	Deisenroth, Faisal & Ong	27–28
6 390 lecture notes spring24	—	101, 117–118
7.pen and paper exercise in ML	—	24–26, 29–33
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	155–158
grokking-deep-learning	—	~234–235
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	300–305, 432, 448–449
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	254–259, 400, 409
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	58, 86, 96
Language Models Interview Handbook	—	108
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	216–222
Machine learning in action	Peter Harrington	113–122
Machine Learning in Action	Peter Harrington	113–122
Machine Learning in Python	—	270–283, 297–300, 303, 312–317, 325–335, 341–347
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	270–274, 291, 303, 308–309, 313–314, 355–361
Machine-Learning-Systems	—	195–198, 649–655
MachineLearningNotes	—	201–207
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	251–257
Neural Networks and Deep Learning: A Textbook	—	48, 144, 149–152, 162, 165–167, 223–225, 407
Practical Machine Learning with H2O	—	197–199
Pro Machine Learning Algorithms	—	130, 255
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	152–155
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	224, 486
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	95–103
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Jacobian and Hessian matrix; Geometry of gradient vector
UnderstandingDeepLearning 02 09 26 C [Reading]	—	403–407

Taylor Series & Approximation (5 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	145–146, 167
5.math4ml	Deisenroth, Faisal & Ong	29–30
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	37–38
mml-book [Reading]	—	171–175
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Taylor series expansion

Integration (19 books)

Book	Author	Pages / Concepts
11.context-engineering	—	46, 52, 56
13.Machine-Learning-Systems	—	137, 357–358, 424, 556–557, 697–699, 878, 1187, 1282–1285, 1318–1322, 1328–1329, 1463, 1749, 1796, 1835
7.pen and paper exercise in ML	—	175, 179
8.matrixcookbook	Petersen & Pedersen	62
9.finetuning guide	—	103
Essential Math for AI	Hala Nelson	Random Variable, Expectation, and Integr
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	193, 204
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	33
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	206–207
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	285–286
Machine learning in action	Peter Harrington	340
Machine Learning in Action	Peter Harrington	340
Machine Learning in Healthcare Informatics	—	19
Machine-Learning-Systems	—	120–121, 339–340, 406, 538–540, 680–682, 860, 1169, 1262–1265, 1298–1302, 1308, 1443, 1747, 1794, 1839–1840
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	277–278, 299–324
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI	—	82–83
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	71
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	100–102, 121
Predictive marketing : easy ways every marketer can use customer analytics and big data	—	78–80

Numerical Methods & Stability (8 books)

Book	Author	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	6. Numerical Methods: Solving Equations
Essential Math for AI	Hala Nelson	Finite Differences
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	814
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	32, 37–38, 44–45
Neural Networks and Deep Learning: A Textbook	—	408
Practical Linear Algebra for Data Science - Mike X Cohen	—	Numerical Stability of the Inverse
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	587
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	216

Gradient Descent (27 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	141, 149–150, 163, 172
12.LAEF	—	Stochastic Gradient Descent
3.Reinforcement Learning- An Overview	Kevin Murphy	17, 54–55
6 390 lecture notes spring24	—	24–25, 28–29, 38, 107
9.finetuning guide	—	30–31
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	4.2 Gradient Descent
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~114–122
Artificial Intelligence: With an Introduction to Machine Learning	—	113–116
Essential Math for AI	Hala Nelson	Gradient Descent ω → i+1 = ω → i - η ∇ L; The scale of the features affects the pe; Near the minima (local and/or global), f (+1)
grokking-deep-learning	—	~47, ~58–59, ~72, ~79–85, ~90–93, ~109, ~158–159
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	176–189, 357
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	166–176
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	67–68
introduction-to-algorithms-and-machine-learning	—	65–82, 221–228
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	47–50, 275–277, 291–293, 298, 302–312, 315–316
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	61–68
MachineLearningNotes	—	141–142
Master Machine Learning Algorithms - Discover how they work	—	~30–32, ~36, ~46–49, ~57–59
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	186
Mastering Machine Learning with scikit-learn 2nd edition	—	98–101
mml-book [Reading]	—	233–238
Neural Networks and Deep Learning: A Textbook	—	141–142
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	163–164
Pro Machine Learning Algorithms	—	161–164
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	487
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Stochastic Gradient Descent
UnderstandingDeepLearning 02 09 26 C [Reading]	—	91–99

Stochastic Gradient Descent (SGD) (20 books)

Book	Author	Pages / Concepts
12.LAEF	—	Stochastic Gradient Descent
3.Reinforcement Learning- An Overview	Kevin Murphy	17
6 390 lecture notes spring24	—	28–29
9.finetuning guide	—	30–31
Artificial Intelligence: With an Introduction to Machine Learning	—	116
Essential Math for AI	Hala Nelson	Stochastic Gradient Descent
grokking-deep-learning	—	~109
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	183–189
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	173–176
Machine learning in action	Peter Harrington	118–122
Machine Learning in Action	Peter Harrington	118–122
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	302–309, 313–314
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	66–68
Master Machine Learning Algorithms - Discover how they work	—	~32, ~46–49, ~57–59
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	186
Neural Networks and Deep Learning: A Textbook	—	141–142
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	164
Pro Machine Learning Algorithms	—	161–164
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Stochastic Gradient Descent; Modifications of SGD
UnderstandingDeepLearning 02 09 26 C [Reading]	—	97–99, 301–302

Momentum & Nesterov (11 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	119
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	70–86
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	446–449
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	407–409
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	145
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	301
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	275–277
Neural Networks and Deep Learning: A Textbook	—	156–159
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	341
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Momentum methods
UnderstandingDeepLearning 02 09 26 C [Reading]	—	100–101

AdaGrad / RMSProp / AdaDelta (6 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	120
9.finetuning guide	—	31–32
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	450–452
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	410–411
Neural Networks and Deep Learning: A Textbook	—	158–159
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	165

Adam / Adamax / Nadam (6 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	121–123
9.finetuning guide	—	33
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	453–454
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	412–415
Neural Networks and Deep Learning: A Textbook	—	160
UnderstandingDeepLearning 02 09 26 C [Reading]	—	102–104

Learning Rate & Schedules (13 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	214, 219–221
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~137–162
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	31–37
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	70–86
Essential Math for AI	Hala Nelson	Explaining the Role of the Learning Rate
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	455–457
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	379–380, 416–419
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	68–73
Language Models Interview Handbook	—	124
Neural Networks and Deep Learning: A Textbook	—	155–157
Pro Machine Learning Algorithms	—	165
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	340
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Adaptive learning rate

Newton & Quasi-Newton (BFGS/L-BFGS) (3 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	27–28
Neural Networks and Deep Learning: A Textbook	—	168
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	488

Conjugate Gradient & Line Search (4 books)

Book	Author	Pages / Concepts
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	166–171
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	299–300, 312
Machine Learning: An Algorithmic Perspective, Second Edition	—	~198–203
Neural Networks and Deep Learning: A Textbook	—	165–167

Convex Optimization & Duality (17 books)

Book	Author	Pages / Concepts
5.math4ml	Deisenroth, Faisal & Ong	31–35
6 390 lecture notes spring24	—	39
Advances in Financial Machine Learning	López de Prado	297
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	86–88
An Introduction to Machine Learning - Machine Learning Summer	—	Geometrical view, dual problem, convex o
Essential Math for AI	Hala Nelson	Convex landscapes versus nonconvex lands; Convex Versus Nonconvex Landscapes; Convex to Linear (+1)
Financial Signal Processing and Machine Learning	—	375–378
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	362–371
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	206
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	247–250, 760–763
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	217
Introduction to Machine Learning with Applications in Information Securit	—	127–128, 140–142
Machine Learning Algorithms with Applications in Finance	—	34, 98–135
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	41–43, 84–86, 120–122, 315–316, 368–370
mml-book [Reading]	—	242–251
Statistical Machine Learning	—	Convexification
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Convex functions; Properties of convex functions; Convex optimization

Lagrange Multipliers & KKT (7 books)

Book	Author	Pages / Concepts
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	41
Essential Math for AI	Hala Nelson	Optimization: Finite Dimensions, Constra; The meaning of Lagrange multipliers; Duality, Lagrange Relaxation, Shadow Pri (+1)
Introduction to Machine Learning with Applications in Information Securit	—	123–128
MachineLearningNotes	—	161–162, 171–176
mml-book [Reading]	—	239–241
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	490
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Function optimization with constraints: ; The Lagrange dual function; Karush-Kuhn-Tucker conditions (KKT)

Coordinate Descent (0 books) No dedicated section found in the indexed tables of contents. Constrained & Linear Programming (8 books)

Book	Author	Pages / Concepts
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	56–58
Essential Math for AI	Hala Nelson	The Simplex Method; The main idea of the simplex method; The simplex method hops around the corne (+1)
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	59–60
Introduction to Machine Learning with Applications in Information Securit	—	121–122
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	568–570, 575–577
introduction-to-algorithms-and-machine-learning	—	147–164
mml-book [Reading]	—	239–241
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	185–228

Evolutionary / Genetic Optimization (13 books)

Book	Author	Pages / Concepts
Artificial Intelligence: With an Introduction to Machine Learning	—	21, 366, 369, 392
Blockchain Enabled Applications: Understand the Blockchain Ecosystem and How to Make it Work for You	—	203
Demystifying Big Data and Machine Learning for Healthcare	—	30–35
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions	—	43–44, 66, 75–84, 142–151
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	171–172
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	110, 121, 210, 291–299, 354, 359
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~179
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	82
Machine learning in bioinformatics	—	229–240
Machine Learning in Healthcare Informatics	—	31, 75, 280–281
Machine Learning: An Algorithmic Perspective, Second Edition	—	~207–208, ~211–215, ~220–224, ~227
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	674
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	352–355, 388

Probability Fundamentals (46 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	61, 79–82
5.math4ml	Deisenroth, Faisal & Ong	37–41
7.pen and paper exercise in ML	—	149–153, 158, 176–178
8.matrixcookbook	Petersen & Pedersen	34
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~41–52, ~55–57
Advances in Financial Machine Learning	López de Prado	191–192, 292–294
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	2. Probability and Statistics: Understan
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	26, 38–39, 56–58
An Introduction to Machine Learning - Machine Learning Summer	—	L1: Machine learning and probability the; Introduction to pattern recognition, cla
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	2.2 Random Variable
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	502–508, 558–564, 1076–1078
Artificial Intelligence: With an Introduction to Machine Learning	—	130–159, 185–186, 203–204, 273–275, 335–336, 443–444
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~159–162, ~343–345
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	157–160
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	160–162
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	103
Essential Math for AI	Hala Nelson	The Vocabulary of Data Distributions, Pr; Random Variables; Probability Distributions (+1)
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	372–381
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	199
grokking-deep-learning	—	~161
Handbook of Statistics: Machine Learning: Theory and Applications	—	~61–100
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	210–211, 265
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	192, 227
Introduction to Artificial Intelligence	—	140–155
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	26, 29
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	134–137
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	398–402
LLM Interview	—	13–14
Machine learning con Python: costruire algoritmi per generare conoscenza	—	101–105, 555
Machine Learning for Hackers	Conway & White	93
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	93
Machine learning in action	Peter Harrington	88–93, 96–97, 368–371
Machine Learning in Action	Peter Harrington	88–93, 96–97, 368–371
Machine Learning: An Algorithmic Perspective, Second Edition	—	~27–31
Machine Learning: Hands-On for Developers and Technical Professionals	—	98, 102
MachineLearningNotes	—	43–52
Master Machine Learning Algorithms - Discover how they work	—	~52, ~94, ~2016
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	227–230
mml-book [Reading]	—	178–188
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	44
Principles And Theory For Data Mining And Machine Learning	—	260–263
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	34, 40–42, 48–58, 63, 66, 75–82, 86, 105, 108, 123–124, 145–155, 175, 218–233, 245, 250, 274–280, 286–288, 310, 313–317, 362, 462, 580–581, 611–612, 621–622, 650–651, 753, 756
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	83, 105, 178
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	4. Basic Statistics and Probability Theo; Probability and odds; Conditional probability (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	463–466, 471–476
Why Machines Learn The Elegant Math Behind Modern AI - Anil Ananthaswamy [Reading]	—	Chapter 4: In All Probability

Probability Distributions (42 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	71, 93, 161–162, 191–199
13.Machine-Learning-Systems	—	1034, 1331–1333, 1489, 1506–1510
3.Reinforcement Learning- An Overview	Kevin Murphy	64–65, 97
5.math4ml	Deisenroth, Faisal & Ong	39–41, 45–46
7.pen and paper exercise in ML	—	56, 120–121, 146–148, 156–157, 182–184, 193–194
8.matrixcookbook	Petersen & Pedersen	37, 40–41, 44–45, 64–65
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~53–54
Advances in Financial Machine Learning	López de Prado	357–358
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	38–39, 60, 65–66, 93–98
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~234–238, ~245–252
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	509–512, 537–540, 1076–1078
Artificial Intelligence: With an Introduction to Machine Learning	—	138–142, 180–182, 203–204, 273–274
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	161–162
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	245, 257–269
Essential Math for AI	Hala Nelson	The Vocabulary of Data Distributions, Pr; Probability Distributions; The Uniform and the Normal Distributions (+1)
Financial Signal Processing and Machine Learning	—	286–291, 294–325, 332–337
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	39–40, 191–192, 200, 207, 211–216
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	237–238
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	209–210, 311–316, 320, 793
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	151–156, 162–163
Introduction to Machine Learning with Applications in Information Securit	—	182–188
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	229
Machine Learning in Healthcare Informatics	—	44
Machine Learning Mastery with Python	Jason Brownlee	43–45
Machine Learning: An Algorithmic Perspective, Second Edition	—	~153–157, ~310–312, ~395–411
Machine-Learning-Systems	—	1016, 1311–1313, 1469, 1486–1490
MachineLearningNotes	—	44–45, 49–50, 68, 80–84, 105, 182, 236
Master Machine Learning Algorithms - Discover how they work	—	~85, ~93–96
mml-book [Reading]	—	178–183, 203–210, 354–355
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI	—	37
Practical Linear Algebra for Data Science - Mike X Cohen	—	Gaussian Elimination
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	81, 101
Practical Machine Learning with H2O	—	137–139
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	44, 136, 246, 327–328, 471
Principles And Theory For Data Mining And Machine Learning	—	353–358
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	43, 59–62, 76–82, 86–96, 105, 108–122, 125–129, 145–147, 158, 163, 175–176, 179–180, 190–208, 246, 313–314, 359–365, 433–439, 481–482, 560, 564, 634–636, 669, 742, 745–748, 756
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	144, 180, 218–220
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	69–72
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	83
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	27–29
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Discrete probability distributions; Bernoulli and categorical distribution; Binomial distribution (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	456, 463–466, 471–476

Bayes' Theorem & Bayesian Inference (53 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	58, 61–67, 71–75, 79–87, 93–98, 369–372
3.Reinforcement Learning- An Overview	Kevin Murphy	17, 22–25
5.math4ml	Deisenroth, Faisal & Ong	38, 45–46
7.pen and paper exercise in ML	—	148, 156–160, 193–194, 205–210
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~95–103, ~122–130, ~139, ~249–251
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	15–16, 20, 25, 61–63
An Introduction to Machine Learning - Machine Learning Summer	—	Introduction to pattern recognition, cla
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	2.4 Bayes' Rule
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~218–220, ~289–309
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	514–517, 532–536, 541–557, 609–617
Artificial Intelligence: With an Introduction to Machine Learning	—	135–137, 162–169, 175–176, 180–182, 196, 276, 291–297
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~261–272
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	271–284
Deep Learning. Practical Neural Networks with Java	—	314–317
Essential Math for AI	Hala Nelson	Conditional Probabilities and Bayes’ The; Conditional Probabilities and Joint Dist; Prior Distribution, Posterior Distributi (+1)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	3–4, 108–110, 161–164, 199
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	221
Handbook of Statistics: Machine Learning: Theory and Applications	—	~381–398
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	320
Introduction to Artificial Intelligence	—	143–148, 155, 171–182, 228–234, 254
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	29, 97–98, 204–206
Introduction to Machine Learning with Applications in Information Securit	—	222–223
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	130–133, 136–137
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	155, 306–318, 326–329
introduction-to-algorithms-and-machine-learning	—	251–258
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~145–146
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	82
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	193–199
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	67–69
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	147–148, 154–155
Machine Learning for Hackers	Conway & White	93–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	93–100
Machine learning in action	Peter Harrington	88–93, 101–104
Machine Learning in Action	Peter Harrington	88–93, 101–104
Machine Learning in Healthcare Informatics	—	123, 129, 274–277
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~69–79, ~98, ~324–327
Machine Learning: An Algorithmic Perspective, Second Edition	—	~322–329
Machine Learning: Hands-On for Developers and Technical Professionals	—	95, 98–101, 105–106
MachineLearningNotes	—	47, 76, 85–89, 106, 130
Master Machine Learning Algorithms - Discover how they work	—	~82–96
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	77
Mastering Machine Learning with scikit-learn 2nd edition	—	127–135
mml-book [Reading]	—	189–191, 309–318
Practical Machine Learning with H2O	—	325–326
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	44–45
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	161
Principles And Theory For Data Mining And Machine Learning	—	225–229, 256–259, 325–326, 349–350, 472–474, 596–597, 645–659, 663–665, 732–735, 740–748
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	46, 63–66, 79–82, 123–124, 130–135, 577–581, 598, 621, 650–651, 657–659, 664, 674
Real-World Machine Learning	—	203–206
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	162–165, 181–182
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	275
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	133, 143–152
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Conditional probability; Bayes theorem; Bayesian Decision Theory (+1)

Maximum Likelihood (MLE) & MAP (19 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	68–70, 88–89
5.math4ml	Deisenroth, Faisal & Ong	44–46
7.pen and paper exercise in ML	—	146–155, 170–171
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~39–40, ~67–74, ~117–118, ~131–132
Advances in Financial Machine Learning	López de Prado	349–350
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	96–98
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	163–164
Essential Math for AI	Hala Nelson	Prior Distribution, Posterior Distributi; Maximum Likelihood Estimation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	185–186, 191
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	220
Introduction to Machine Learning with Applications in Information Securit	—	173
MachineLearningNotes	—	73–75, 123–126
mml-book [Reading]	—	319–320, 356–365
Neural Networks and Deep Learning: A Textbook	—	409–410
Predictive marketing : easy ways every marketer can use customer analytics and big data	—	147–160
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	561–564, 568, 573–574, 580–581, 591–594, 598, 616–617, 621, 627–628, 650–651
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	207
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Likelihood function; Method of Maximum Likelihood Estimation ; Training CTC network: Maximum likelihood
UnderstandingDeepLearning 02 09 26 C [Reading]	—	70–73

Expectation, Variance & Covariance (44 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	59–60, 76–78
12.LAEF	—	Covariance & Correlation
5.math4ml	Deisenroth, Faisal & Ong	41–43
7.pen and paper exercise in ML	—	148
8.matrixcookbook	Petersen & Pedersen	34–35, 42–43
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~75
Advances in Financial Machine Learning	López de Prado	320–322
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	106–107, 116, 126–141, 145
Artificial Intelligence: With an Introduction to Machine Learning	—	248–249, 257–259
Basics of Linear Algebra for Machine Learning	Jason Brownlee	152–159
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	163–164
Essential Math for AI	Hala Nelson	Expectation, Mean, Variance, and Uncerta; Covariance and Correlation; Multivariate statistics: Wishart matrice (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	47, 119
Financial Signal Processing and Machine Learning	—	157–168
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	52–59, 74, 79–95
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	323, 328
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	235, 271, 274
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	30, 76, 90–91, 150
Introduction to Machine Learning with Applications in Information Securit	—	89–91
introduction-to-algorithms-and-machine-learning	—	207–220
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	230–234
Machine learning in action	Peter Harrington	197–198, 330–331
Machine Learning in Action	Peter Harrington	197–198, 330–331
Machine Learning in Python	—	263–264
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~316–318
Machine Learning Yearning (Draft Version)	Andrew Ng	42–45, 49–50, 53–58, 79–80
Machine Learning: An Algorithmic Perspective, Second Edition	—	~35
Machine Learning: Hands-On for Developers and Technical Professionals	—	52
MachineLearningNotes	—	66–67, 127–128
Master Machine Learning Algorithms - Discover how they work	—	~19–20
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	236
Mastering Machine Learning with scikit-learn 2nd edition	—	25–26, 232–233
mml-book [Reading]	—	326–330
Neural Networks and Deep Learning: A Textbook	—	193, 211
Practical Linear Algebra for Data Science - Mike X Cohen	—	Multivariate Data Covariance Matrices; Covariance and Correlation Matrices Exer; Converting Singular Values to Variance,
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	167
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	302–305
Principles And Theory For Data Mining And Machine Learning	—	146–147
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	83–85, 106–107, 115, 171–172, 218–224, 231, 247, 259–261, 587, 592–593, 597, 678, 682–683
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	83–85, 143
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	41, 44–47, 122, 213
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	117–119
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Covariance matrix; Moments; Mathematical expectation of a random var (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	467–470

Correlation (27 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	251–257, 308–312
12.LAEF	—	Covariance & Correlation
5.math4ml	Deisenroth, Faisal & Ong	43
Advances in Financial Machine Learning	López de Prado	319
Basics of Linear Algebra for Machine Learning	Jason Brownlee	157–158
Essential Math for AI	Hala Nelson	Covariance and Correlation; Convolution and Cross-Correlation
Financial Signal Processing and Machine Learning	—	247–260
grokking-deep-learning	—	~110, ~116–117, ~123, ~135, ~192
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	399–403
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	95–98
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	91–93
Introduction to Artificial Intelligence	—	269–270
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	169
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	83–84
Machine Learning for Hackers	Conway & White	168–170
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	168–170
Machine Learning in Python	—	83, 94–95
Machine Learning Mastery with Python	Jason Brownlee	44
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	147, 151, 207
Practical Linear Algebra for Data Science - Mike X Cohen	—	Correlation and Cosine Similarity; Correlation Exercises; Covariance and Correlation Matrices Exer
Practical Machine Learning with H2O	—	115–118
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	331
Predictive Analytics	—	108–112
Pro Machine Learning Algorithms	—	34
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	84–85, 182–184, 405–412, 573–575
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	44–47, 243
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Correlation matrix; Correlation

Hypothesis Testing & p-values (8 books)

Book	Author	Pages / Concepts
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	150–156
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	98–99
Machine Learning in Healthcare Informatics	—	303
MachineLearningNotes	—	62–70
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	137
Principles And Theory For Data Mining And Machine Learning	—	475–476, 694–696, 706–707, 719
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	190–193, 571–572, 588
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Hypothesis testing

Confidence Intervals & Sampling (35 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2023–2025
3.Reinforcement Learning- An Overview	Kevin Murphy	24–25, 59
7.pen and paper exercise in ML	—	123–126, 175–188
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~154–164
Advances in Financial Machine Learning	López de Prado	69–72, 135–139
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	23, 38–39
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~310–311
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~330
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	102–107
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	175–178, 355–356
Handbook of Statistics: Machine Learning: Theory and Applications	—	~3–18
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	333–334
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	531–534
LLM Interview	—	15–16
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	97–100
Machine learning con Python: costruire algoritmi per generare conoscenza	—	310–315
Machine learning in action	Peter Harrington	175–177
Machine Learning in Action	Peter Harrington	175–177
Machine learning in bioinformatics	—	89–110
Machine Learning in Python	—	260–262
Machine Learning Mastery with Python	Jason Brownlee	66
Machine Learning: An Algorithmic Perspective, Second Edition	—	~227, ~305–307
MachineLearningNotes	—	21, 228–229
Master Machine Learning Algorithms - Discover how they work	—	~130–135
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	235
Neural Networks and Deep Learning: A Textbook	—	229–230, 399
Practical Machine Learning with H2O	—	155–156
Principles And Theory For Data Mining And Machine Learning	—	33–41, 56–57
Pro Machine Learning Algorithms	—	187–189
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	674
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	101–102
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	85–87
Statistical Machine Learning	—	Planification expérimentale - Validation
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Sampling from known distributions; Sampling and quantization
UnderstandingDeepLearning 02 09 26 C [Reading]	—	474–476

Monte Carlo & MCMC (13 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
3.Reinforcement Learning- An Overview	Kevin Murphy	35, 59, 72
7.pen and paper exercise in ML	—	175–179, 189–192, 195–198
Advances in Financial Machine Learning	López de Prado	313–315, 323–325
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	23, 40–41, 49–55
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	107
Essential Math for AI	Hala Nelson	Monte Carlo Methods
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	425–426
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	333–334
Machine Learning: An Algorithmic Perspective, Second Edition	—	~305–309, ~313–318
Neural Networks and Deep Learning: A Textbook	—	414
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	156, 598, 665–671, 674
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	101–102

Entropy, KL Divergence & Information Theory (26 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	39, 55–57, 102–134, 364–365
3.Reinforcement Learning- An Overview	Kevin Murphy	28
9.finetuning guide	—	58
Advances in Financial Machine Learning	López de Prado	348, 357–364
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	9. Information Theory: Quantifying and E
Artificial Intelligence: With an Introduction to Machine Learning	—	117–122
Essential Math for AI	Hala Nelson	Entropy and Gini impurity; Entropy and information gain
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	203
grokking-deep-learning	—	~258–259
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	245–246, 453
Handbook of Statistics: Machine Learning: Theory and Applications	—	~19–60
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	269
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	229–231
Introduction to Artificial Intelligence	—	149–154, 213–217
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	97
Language Models Interview Handbook	—	107–109
Machine learning in action	Peter Harrington	67–69
Machine Learning in Action	Peter Harrington	67–69
Machine Learning in Healthcare Informatics	—	322
MachineLearningNotes	—	53–54
Mastering Machine Learning with scikit-learn 2nd edition	—	143–147
Pro Machine Learning Algorithms	—	89–90
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	289, 589–590
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	174–176
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Information theory; Entropy; Relative entropy or KL divergence (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	85

Cross-Entropy & Log Loss (5 books)

Book	Author	Pages / Concepts
9.finetuning guide	—	58
grokking-deep-learning	—	~258–259
Handbook of Statistics: Machine Learning: Theory and Applications	—	~19–60
Language Models Interview Handbook	—	107
UnderstandingDeepLearning 02 09 26 C [Reading]	—	85

Data Cleaning & Missing Values (29 books)

Book	Author	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~39–40, ~76–82, ~260–263
Artificial Intelligence: With an Introduction to Machine Learning	—	281–289, 301–307
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	312–314
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	112–114
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	143–145
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	190
grokking-deep-learning	—	~145
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	102–104
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	50
Introduction to Artificial Intelligence	—	225
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	195–197
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	100
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	89–92, 120–121
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	295–301
Machine learning in action	Peter Harrington	124
Machine Learning in Action	Peter Harrington	124
Machine Learning in Healthcare Informatics	—	228
Machine Learning: Hands-On for Developers and Technical Professionals	—	54, 61
MachineLearningNotes	—	20
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	139–140, 278–279, 336
Neural Networks and Deep Learning: A Textbook	—	201, 221–222
Practical Linear Algebra for Data Science - Mike X Cohen	—	Noise Reduction
Practical Machine Learning with H2O	—	119, 126–127, 304–306
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	70, 111, 164–165, 397
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	66–72, 137–138
Principles And Theory For Data Mining And Machine Learning	—	25, 172–173
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	526–528, 597, 696–711
Real-World Machine Learning	—	61–62
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	211

Scaling, Normalization & Standardization (41 books)

Book	Author	Pages / Concepts
11.context-engineering	—	51
12.LAEF	—	Preprocessing & Scaling
13.Machine-Learning-Systems	—	224, 432–433, 444–446, 584, 675, 730–731, 738–740, 1025–1027, 1074–1075, 1083, 1123, 1633–1634, 1736, 1821
2.Foundation of LLM	—	70–72
6 390 lecture notes spring24	—	63, 121–123
9.finetuning guide	—	98
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	261
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	146–147, 326–327
Essential Math for AI	Hala Nelson	Normalizing, Scaling, and/or Standardizi; Batch Normalization of Each Layer
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	45–50, 77–78, 81, 167–168
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	68
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	319–320, 327–329, 484, 638–639
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	108, 426–431
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	103–106, 395–399, 488–490
Introduction to Machine Learning with Applications in Information Securit	—	41–42
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~132–139
Language Models Interview Handbook	—	27, 36, 110–111
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	89–92
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	318–322
Machine Learning for Hackers	Conway & White	232–237
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	232–237
Machine learning in action	Peter Harrington	56–57
Machine Learning in Action	Peter Harrington	56–57
Machine Learning in Healthcare Informatics	—	18
Machine Learning Mastery with Python	Jason Brownlee	58–59, 145–146, 164–165
Machine-Learning-Systems	—	208, 414–415, 426–428, 567, 657–658, 712, 720–722, 1007–1009, 1055–1056, 1065, 1104–1105, 1613–1614, 1720, 1819
MachineLearningNotes	—	38–39, 255–256
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	141–142, 190
Mastering Machine Learning with scikit-learn 2nd edition	—	56–58, 61
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	68–69
Neural Networks and Deep Learning: A Textbook	—	172–175, 307, 347
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	70
Practical Machine Learning with Python	—	~239–241, ~333–335
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	167, 258–260, 350–352
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	79
Principles And Theory For Data Mining And Machine Learning	—	560–565
Pro Machine Learning Algorithms	—	169–172, 300–302, 312–313
Real-World Machine Learning	—	48–49, 65, 207, 219–223, 226–233
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	62
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Orthonormalization; Applications of Orthonormalization
UnderstandingDeepLearning 02 09 26 C [Reading]	—	206–208, 318–320

Categorical Encoding (19 books)

Book	Author	Pages / Concepts
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	79–81
Basics of Linear Algebra for Machine Learning	Jason Brownlee	29
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	149–151
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	93–94, 99
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	494–498
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	168
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~212–219
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	104
Machine learning con Python: costruire algoritmi per generare conoscenza	—	166–168
Machine Learning for Hackers	Conway & White	148
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	148
Machine Learning in Python	—	259
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	139–140
Mastering Machine Learning with scikit-learn 2nd edition	—	60
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	89
Practical Machine Learning with Python	—	~200–208
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	166, 219, 222–224
Real-World Machine Learning	—	59–60, 163–164
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	One hot encoding

Outlier Detection & Handling (15 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2023–2025, 2167–2168
Deep Learning. Practical Neural Networks with Java	—	367–370, 379–385
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	316, 321
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	261
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~199
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	97–98, 103–106, 111–112, 137, 141
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	66–80, 216–218, 311–313
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	130–132
Machine Learning in Python	—	69–70
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	86, 171
Neural Networks and Deep Learning: A Textbook	—	100
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	62, 329–330
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	76–78
Pro Machine Learning Algorithms	—	84, 116
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	307–308

Data Transformation & Discretization (18 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	430–431, 434
Advances in Financial Machine Learning	López de Prado	194
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~305–306, ~314–321
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	296–304
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	148
Essential Math for AI	Hala Nelson	Discretize right away and do a computer ; Discretization and the Curse of Dimensio; Example: Discretize the one-dimensional
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	26–44
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	145–147
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	491–492
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~220–223
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	101
Machine Learning Mastery with Python	Jason Brownlee	56
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	188–189, 228–229
Machine-Learning-Systems	—	412–413, 416
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	210–216, 246
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	92–94
Real-World Machine Learning	—	82–83
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	70

Handling Imbalanced Data (11 books)

Book	Author	Pages / Concepts
9.finetuning guide	—	19–20, 23
Advances in Financial Machine Learning	López de Prado	110
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	8.1 Handling Imbalanced Datasets
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	122–132
Introduction to Machine Learning with Applications in Information Securit	—	247–248
Language Models Interview Handbook	—	49
Machine learning in action	Peter Harrington	169, 175–177
Machine Learning in Action	Peter Harrington	169, 175–177
Machine Learning in Python	—	339–340
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	231–233
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	80–81

Feature Extraction (14 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	222, 366–368
13.Machine-Learning-Systems	—	2469–2492
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	97–98, 146, 152
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	149–154
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	670–673, 679
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~140–167
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	94, 105–106
Machine Learning in Healthcare Informatics	—	290, 296, 315–317
Machine Learning in Python	—	51
Machine Learning Mastery with Python	Jason Brownlee	98
Mastering Machine Learning with scikit-learn 2nd edition	—	60
Practical Machine Learning with Python	—	~181–184
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	78–80, 200–201, 250–251, 269–270
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	28, 41, 150, 258–259, 266–268, 272

Feature Selection (22 books)

Book	Author	Pages / Concepts
Advances in Financial Machine Learning	López de Prado	158–167
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	162–163
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	54
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	293–294
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	249
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	69
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	23–24, 164, 188, 198
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~236–241
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	94–97, 111–117, 121–122
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	77–78, 189–191
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	309–310
Machine learning in bioinformatics	—	1–46, 135–156, 301–320
Machine Learning in Healthcare Informatics	—	199, 229, 252, 300–302, 318, 326–330
Machine Learning Mastery with Python	Jason Brownlee	61–64
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	242
Neural Networks and Deep Learning: A Textbook	—	90
Practical Machine Learning with Python	—	~242–248
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	261, 266
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	82–86, 139–142, 175–176, 210–215, 241–242
Principles And Theory For Data Mining And Machine Learning	—	582, 628–629, 645–647
Real-World Machine Learning	—	139–141, 144–147
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	151

Principal Component Analysis (PCA) (34 books)

Book	Author	Pages / Concepts
12.LAEF	—	Principal Component Analysis; Principal Components; Beyond Linear PCA
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~239–247, ~252–259
Basics of Linear Algebra for Machine Learning	Jason Brownlee	30, 163–166
Essential Math for AI	Hala Nelson	Principal Component Analysis and Dimensi; Principal Component Analysis and Cluster
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	115–116, 120–126
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	295–297
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	322–325, 331–335, 630–631
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	271–272, 277–279, 667
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	139, 165–166
Introduction to Machine Learning with Applications in Information Securit	—	82, 92–97, 296–297, 304–307
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	123, 321–322
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	258
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	219–220
Machine learning con Python: costruire algoritmi per generare conoscenza	—	198–200, 225, 233, 244–245
Machine Learning for Hackers	Conway & White	221–230
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	221–230
Machine learning in action	Peter Harrington	296–304
Machine Learning in Action	Peter Harrington	296–304
Machine Learning in Healthcare Informatics	—	43, 79
Machine Learning Mastery with Python	Jason Brownlee	63
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	331–337
Machine Learning: An Algorithmic Perspective, Second Edition	—	~133–140
MachineLearningNotes	—	40
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	223–225
Mastering Machine Learning with scikit-learn 2nd edition	—	227–231, 236–242
mml-book [Reading]	—	323, 341–344
Practical Linear Algebra for Data Science - Mike X Cohen	—	Statistics (Principal Components Analysi; PCA Using Eigendecomposition and SVD; The Math of PCA (+1)
Practical Machine Learning with H2O	—	299–300
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	269–270
Principles And Theory For Data Mining And Machine Learning	—	508
Pro Machine Learning Algorithms	—	292–305
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	405–409
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	152, 159
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Principal Component Analysis; Reducing with principal components; When to use PCA (+1)

Kernel PCA & Manifold Learning (11 books)

Book	Author	Pages / Concepts
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	298–300
Handbook of Statistics: Machine Learning: Theory and Applications	—	~471–492
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	320–321, 335, 339–341
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	269–270, 282–283
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~140–167
Machine learning con Python: costruire algoritmi per generare conoscenza	—	225, 233, 244–245
Machine Learning for Hackers	Conway & White	231, 243–248
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	231, 243–248
Machine Learning: An Algorithmic Perspective, Second Edition	—	~144–149
Principles And Theory For Data Mining And Machine Learning	—	560–565
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	62

Linear Discriminant Analysis (LDA) (7 books)

Book	Author	Pages / Concepts
Introduction to Machine Learning with Applications in Information Securit	—	211–220
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~101–108, ~126
Machine Learning: An Algorithmic Perspective, Second Edition	—	~39–42, ~130–132
MachineLearningNotes	—	133–134
Master Machine Learning Algorithms - Discover how they work	—	~61, ~65–66
Practical Linear Algebra for Data Science - Mike X Cohen	—	Linear Discriminant Analysis; Linear Discriminant Analyses
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	273–274

t-SNE & UMAP (1 books)

Book	Author	Pages / Concepts
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	t-SNE; PCA vs t-SNE; t-SNE on Iris Dataset

Independent Component Analysis (ICA) (5 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	164–165
Essential Math for AI	Hala Nelson	Explicit Density-Tractable: Change of Va
Machine Learning: An Algorithmic Perspective, Second Edition	—	~142–143
Principles And Theory For Data Mining And Machine Learning	—	524–525, 529–531
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	244

Linear Regression / OLS (47 books)

Book	Author	Pages / Concepts
12.LAEF	—	Least Squares Approximation; Regularized Least Squares
6 390 lecture notes spring24	—	16–19, 117–118, 136
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~1–11, ~33–34
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	185–186
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.1 Linear Regression
Artificial Intelligence: With an Introduction to Machine Learning	—	106–108, 111–112
Basics of Linear Algebra for Machine Learning	Jason Brownlee	25, 29, 169–172
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	173–178
Essential Math for AI	Hala Nelson	For linear regression, the loss function; When do we use plain linear regression,
Financial Signal Processing and Machine Learning	—	403–405, 415–416
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	170–171, 355–356
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	160–161
Introduction to Artificial Intelligence	—	276
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	65–68
introduction-to-algorithms-and-machine-learning	—	167–182
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~139–140
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	150–178
Machine learning con Python: costruire algoritmi per generare conoscenza	—	397–401
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	142
Machine Learning for Hackers	Conway & White	149–156
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	149–156
Machine learning in action	Peter Harrington	181–189
Machine Learning in Action	Peter Harrington	181–189
Machine Learning in Healthcare Informatics	—	169
Machine Learning in Python	—	155–161, 166–169, 185–188, 200, 215–224
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~129–144, ~178–180
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	67–72, 80
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems	—	52–71
Machine Learning with TensorFlow	—	~53, ~59–61, ~69, ~78–82
Machine Learning: An Algorithmic Perspective, Second Edition	—	~64–66
Machine Learning: Hands-On for Developers and Technical Professionals	—	355
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	57–59
Master Machine Learning Algorithms - Discover how they work	—	~34–37, ~40–42, ~46–49
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	167–169, 177
Mastering Machine Learning with scikit-learn 2nd edition	—	32–36, 39–40, 82–85, 92, 103
mml-book [Reading]	—	295–296, 309–318
Practical Linear Algebra for Data Science - Mike X Cohen	—	11. General Linear Models and Least Squa; A Geometric Perspective on Least Squares; Why Does Least Squares Work? (+1)
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	137, 336
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	151–152
Principles And Theory For Data Mining And Machine Learning	—	536, 583–584
Pro Machine Learning Algorithms	—	33–46, 52–65, 72–73
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	685
Python Machine Learning	—	134–164
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	194–195, 198, 223, 226, 310–312, 489
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	275
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Linear and curvilinear regression; OLS model
UnderstandingDeepLearning 02 09 26 C [Reading]	—	32–35

Polynomial Regression (10 books)

Book	Author	Pages / Concepts
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	255–257
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	190–192
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	177–178
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	149
Machine Learning for Hackers	Conway & White	174–180
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	174–180
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	69–70
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	157–160
Mastering Machine Learning with scikit-learn 2nd edition	—	86–90
Practical Linear Algebra for Data Science - Mike X Cohen	—	Polynomial Regression; Polynomial Regression Exercise

Ridge / Lasso / Elastic Net (16 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	20, 27, 62
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	61–63
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~192–210
Essential Math for AI	Hala Nelson	When do we use plain linear regression,
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	200–206
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	184–189
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	76–78, 189–190
Machine Learning for Hackers	Conway & White	206–208
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	206–208
Machine learning in action	Peter Harrington	191–196
Machine Learning in Action	Peter Harrington	191–196
Machine Learning in Healthcare Informatics	—	199–200
Machine Learning in Python	—	144–152, 163–165
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	72–73
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	211–212, 273
Statistical Machine Learning	—	Régression ordinale et Régression (Lasso

Logistic Regression (38 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	28, 33–38, 50–54, 362–363
6 390 lecture notes spring24	—	38, 137–138
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.2 Logistic Regression
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~70–79, ~391–394
Artificial Intelligence: With an Introduction to Machine Learning	—	115, 409
Deep Learning. Practical Neural Networks with Java	—	622–627
Essential Math for AI	Hala Nelson	Logistic Regression: Classify into Two C
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	82–87
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	222–223
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	209
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	192
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	80, 83, 199–201
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	175–177
introduction-to-algorithms-and-machine-learning	—	193–206
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~141–142
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	184–188
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	58–59, 362–363
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	143–144
Machine Learning for Hackers	Conway & White	194–198
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	194–198
Machine learning in action	Peter Harrington	110–112, 125–126
Machine Learning in Action	Peter Harrington	110–112, 125–126
Machine Learning in Healthcare Informatics	—	278–279
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~181–193, ~218
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	81–83, 118–122, 128–129
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems	—	72–100
Machine Learning with TensorFlow	—	~83–89
Master Machine Learning Algorithms - Discover how they work	—	~51–61
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	179–181, 189–190
Mastering Machine Learning with scikit-learn 2nd edition	—	103–105
Neural Networks and Deep Learning: A Textbook	—	81–82, 88
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	137–139
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	280
Pro Machine Learning Algorithms	—	64–65, 68–70, 74–82
Python Machine Learning	—	165–189
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	217
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	275
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Logistic Regression; Multiclass logistic regression

Generalized Linear Models (6 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	193–194
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	191–192
Practical Linear Algebra for Data Science - Mike X Cohen	—	GLM in a Simple Example
Practical Machine Learning with H2O	—	222–243
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	137
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Poisson regression

Regression Evaluation Metrics (5 books)

Book	Author	Pages / Concepts
Essential Math for AI	Hala Nelson	For linear regression, the loss function; Minimizing the mean squared error loss f
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	32, 79
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	154–156
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	300, 334–335
Pro Machine Learning Algorithms	—	45, 50

k-Nearest Neighbors (kNN) (30 books)

Book	Author	Pages / Concepts
4.alg4ai	—	48–50
6 390 lecture notes spring24	—	80
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	116
An Introduction to Machine Learning - Machine Learning Summer	—	Nearest Neighbor, Kernels density estima
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.5 k-Nearest Neighbors
Introduction to Artificial Intelligence	—	202–205, 236, 252
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	178, 202–203
Introduction to Machine Learning with Applications in Information Securit	—	196–197
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	172, 198, 222, 265–266
introduction-to-algorithms-and-machine-learning	—	237–250
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~147
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	181
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	70
Machine Learning for Hackers	Conway & White	249–254
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	249–254
Machine learning in action	Peter Harrington	45, 50–51, 62
Machine Learning in Action	Peter Harrington	45, 50–51, 62
Machine Learning in Healthcare Informatics	—	193, 280–281
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~47–65
MachineLearningNotes	—	22–25
Master Machine Learning Algorithms - Discover how they work	—	~98–109
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	201–202
Mastering Machine Learning with scikit-learn 2nd edition	—	44–55
Principles And Theory For Data Mining And Machine Learning	—	111–114
Pro Machine Learning Algorithms	—	308–309
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	654
Python Machine Learning	—	217–232
Real-World Machine Learning	—	247–248
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	147–149
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	K-nearest neighbor

Naive Bayes (22 books)

Book	Author	Pages / Concepts
Deep Learning. Practical Neural Networks with Java	—	314–317
Essential Math for AI	Hala Nelson	Naive Bayes Classification Model
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	221
Introduction to Artificial Intelligence	—	231–234
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	98
Introduction to Machine Learning with Applications in Information Securit	—	222–223
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	132–133
introduction-to-algorithms-and-machine-learning	—	251–258
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~145–146
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	193–199
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	67–69
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	147–148
Machine learning in action	Peter Harrington	88, 92–93, 101–104
Machine Learning in Action	Peter Harrington	88, 92–93, 101–104
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~69–79, ~98, ~324–327
MachineLearningNotes	—	130
Master Machine Learning Algorithms - Discover how they work	—	~82–96
Mastering Machine Learning with scikit-learn 2nd edition	—	127–135
Practical Machine Learning with H2O	—	325–326
Real-World Machine Learning	—	203–206
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	162–165, 181–182
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Naive Bayes classifier

Decision Trees (CART/ID3/C4.5) (43 books)

Book	Author	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	5. Supervised Learning Algorithms: Regre
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.3 Decision Tree Learning
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	716–726
Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies	—	47–51
Artificial Intelligence: With an Introduction to Machine Learning	—	117–122, 217–230
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	35–39
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~99–107, ~192–202, ~566–570
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	62–64, 97–104, 189–199
Essential Math for AI	Hala Nelson	Decision Trees; Regression decision trees; Shortcomings of decision trees
Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions	—	29–33
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	259–261, 266–267, 717–718
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	223–224, 227, 235
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	249–250
Introduction to Artificial Intelligence	—	211, 218–219, 253
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	92–95
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	210
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	90–94
introduction-to-algorithms-and-machine-learning	—	285–328
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~129–134
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	82
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	189–192
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	60–62
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	140–141
Machine learning in action	Peter Harrington	64–65, 84–85, 211–212
Machine Learning in Action	Peter Harrington	64–65, 84–85, 211–212
Machine Learning in Healthcare Informatics	—	245–246, 255–256, 280–281
Machine Learning in Python	—	246–251
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~11–28, ~41–44
Machine Learning: An Algorithmic Perspective, Second Edition	—	~249–260
Machine Learning: Hands-On for Developers and Technical Professionals	—	71–85, 338
MachineLearningNotes	—	15–18
Master Machine Learning Algorithms - Discover how they work	—	~72–79, ~130–139
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	194
Mastering Machine Learning with scikit-learn 2nd edition	—	137–139, 149–155
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	136
Practical Machine Learning with H2O	—	162–163
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	301, 340–342
Predictive Analytics	—	133–134, 149
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	115–119, 155–156
Principles And Theory For Data Mining And Machine Learning	—	225–229
Pro Machine Learning Algorithms	—	85–88, 99–107, 113, 116
Statistical Machine Learning	—	Méthodes de partitionnement - l'algorith
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Decision tree

Support Vector Machines (SVM) (41 books)

Book	Author	Pages / Concepts
An Introduction to Machine Learning - Machine Learning Summer	—	L4: Support Vector estimation; L5: Support Vector estimation
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	3.4 Support Vector Machine
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	763–766
Essential Math for AI	Hala Nelson	Support Vector Machines
Financial Signal Processing and Machine Learning	—	346–362
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	76
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	263–267
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	141–145
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	225–232, 240–241, 251–254, 715–716, 760–763
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	203–207, 211–216
Introduction to Artificial Intelligence	—	287–288, 299
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	84–87, 191–192
Introduction to Machine Learning with Applications in Information Securit	—	114–120, 129–130, 134, 308, 317–322
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	119, 169–174, 185, 267–268
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~143–144
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	200–203
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	65–66
Machine learning con Python: costruire algoritmi per generare conoscenza	—	125–126
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	145–146
Machine Learning for Hackers	Conway & White	291–299
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	291–299
Machine learning in action	Peter Harrington	128, 345–348
Machine Learning in Action	Peter Harrington	128, 345–348
Machine Learning in Healthcare Informatics	—	46, 167–168, 178, 194, 244
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~221–228, ~243–246
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	123–129
Machine Learning: An Algorithmic Perspective, Second Edition	—	~169, ~179–186
Machine Learning: Hands-On for Developers and Technical Professionals	—	165–167, 170–173
MachineLearningNotes	—	150, 158–165, 168–170, 174–176
Master Machine Learning Algorithms - Discover how they work	—	~115–124
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	198
Mastering Machine Learning with scikit-learn 2nd edition	—	176, 181–183
mml-book [Reading]	—	376–377, 380–393
Neural Networks and Deep Learning: A Textbook	—	30, 83–84, 87, 249
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	137–139
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	158–160
Principles And Theory For Data Mining And Machine Learning	—	277–279, 286–296, 303–308
Python Machine Learning	—	190–216
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	276, 281–287, 292–293, 309
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	276
Statistical Machine Learning	—	SVM

Kernels & Kernel Trick (13 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	46
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~321–322
Essential Math for AI	Hala Nelson	The kernel trick
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	102–104
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	233–234, 237–238
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	208–210
Introduction to Machine Learning with Applications in Information Securit	—	136–138
Machine learning in bioinformatics	—	209–228
Machine Learning: An Algorithmic Perspective, Second Edition	—	~111–118
Mastering Machine Learning with scikit-learn 2nd edition	—	177–180
Neural Networks and Deep Learning: A Textbook	—	57, 236–239, 245–249
Principles And Theory For Data Mining And Machine Learning	—	88–92
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	277–278, 286

Perceptron (23 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	263–269, 316–322, 360–361
13.Machine-Learning-Systems	—	260, 294
An Introduction to Machine Learning - Machine Learning Summer	—	L3: Perceptron and Kernels; Hebb's rule, perceptron algorithm, conve
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	24–25
Artificial Intelligence: With an Introduction to Machine Learning	—	404–409
Deep Learning. Practical Neural Networks with Java	—	515, 520–528
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	390–398
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	332–340
Introduction to Artificial Intelligence	—	196–197, 252, 280
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	19–20, 102–107
Machine learning con Python: costruire algoritmi per generare conoscenza	—	62–71, 94–100, 492–501
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	102–104, 108–111, 116–117, 123–124
Machine Learning: An Algorithmic Perspective, Second Edition	—	~43–54, ~71–72, ~85–88
Machine Learning: Hands-On for Developers and Technical Professionals	—	120–123, 129–130
Machine-Learning-Systems	—	244, 278
MachineLearningNotes	—	137, 143–144
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	317–320
Mastering Machine Learning with scikit-learn 2nd edition	—	161, 164–176, 190, 193–195, 201–204
Neural Networks and Deep Learning: A Textbook	—	25–29, 76–77, 85–86, 245
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	53
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	318
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	277–278
Statistical Machine Learning	—	Le perceptron - méthodes linéaires

Discriminant Analysis (8 books)

Book	Author	Pages / Concepts
Essential Math for AI	Hala Nelson	Topic Vector Representation of a Documen
Introduction to Machine Learning with Applications in Information Securit	—	211–220
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~101–108, ~126
Machine Learning: An Algorithmic Perspective, Second Edition	—	~130–132
Master Machine Learning Algorithms - Discover how they work	—	~61, ~65–66
Practical Linear Algebra for Data Science - Mike X Cohen	—	Linear Discriminant Analysis
Principles And Theory For Data Mining And Machine Learning	—	250–255, 260–263
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	273–274

Multi-class & Multi-label (27 books)

Book	Author	Pages / Concepts
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	7.2 Multiclass Classification; 7.4 Multi-Label Classification
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~90
Essential Math for AI	Hala Nelson	Multi-class output
Explainable and Interpretable Models in Computer Vision and Machine Learning	—	~81–114
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	196–197, 204–215
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	48–51
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	152–154, 161–162
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	147–149, 153–154
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	221–222, 225–229
Language Models Interview Handbook	—	49
Machine learning con Python: costruire algoritmi per generare conoscenza	—	283–284, 555
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	105–107
Machine Learning in Python	—	102–106, 238–242, 336–338
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	128–139, 237–239, 247
Machine Learning with TensorFlow	—	~90–95
Machine Learning: Hands-On for Developers and Technical Professionals	—	166–167
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	46, 51
MachineLearningNotes	—	59, 146–149, 193
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	189
Mastering Machine Learning with scikit-learn 2nd edition	—	116–125
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	151
Neural Networks and Deep Learning: A Textbook	—	85–86
Principles And Theory For Data Mining And Machine Learning	—	248–249, 308
Real-World Machine Learning	—	116–118
Statistical Machine Learning	—	Classification multi-label
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Multiclass logistic regression
UnderstandingDeepLearning 02 09 26 C [Reading]	—	81–82

Base Classifiers / Weak Learners (3 books)

Book	Author	Pages / Concepts
Machine learning in action	Peter Harrington	160–162
Machine Learning in Action	Peter Harrington	160–162
Machine Learning in Python	—	345–347

Bagging & Bootstrap Aggregation (20 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	203–206, 215
6 390 lecture notes spring24	—	86
Advances in Financial Machine Learning	López de Prado	100–106, 135–139, 143–144
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~352–355
Handbook of Statistics: Machine Learning: Theory and Applications	—	~101–150
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	284–287
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	243–245
Machine learning con Python: costruire algoritmi per generare conoscenza	—	310–315
Machine learning in action	Peter Harrington	157
Machine Learning in Action	Peter Harrington	157
Machine Learning in Python	—	260–270, 284, 304–308
Machine Learning Mastery with Python	Jason Brownlee	101–102
Machine Learning: An Algorithmic Perspective, Second Edition	—	~273–274
MachineLearningNotes	—	21, 228–230
Master Machine Learning Algorithms - Discover how they work	—	~126–135
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	240–241, 246
Mastering Machine Learning with scikit-learn 2nd edition	—	153–155
Neural Networks and Deep Learning: A Textbook	—	205
Principles And Theory For Data Mining And Machine Learning	—	327–330
Pro Machine Learning Algorithms	—	120

Random Forests (26 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	86
Advances in Financial Machine Learning	López de Prado	140
Essential Math for AI	Hala Nelson	Random Forests
Handbook of Statistics: Machine Learning: Theory and Applications	—	~101–150
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	279, 291, 719–720
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	239, 248
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	97
Introduction to Machine Learning with Applications in Information Securit	—	205–210
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	167–168, 185
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	214–215
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	138–139
Machine Learning in Healthcare Informatics	—	205
Machine Learning in Python	—	281–292, 296, 309–311, 319–331, 336–338, 345–347
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~247–251, ~260
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems	—	101–122
Machine Learning: An Algorithmic Perspective, Second Edition	—	~275–276
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	47
MachineLearningNotes	—	20, 230
Master Machine Learning Algorithms - Discover how they work	—	~126–135
Mastering Machine Learning with scikit-learn 2nd edition	—	153–155
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI	—	123
Practical Machine Learning with H2O	—	162–165, 169–171, 180–194
Principles And Theory For Data Mining And Machine Learning	—	269–276
Pro Machine Learning Algorithms	—	118–126, 129
Real-World Machine Learning	—	215–217, 249
Statistical Machine Learning	—	Random Forest

Boosting (general) (25 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	203–206, 215
Advances in Financial Machine Learning	López de Prado	141–143
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~358–361
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	134
Handbook of Statistics: Machine Learning: Theory and Applications	—	~101–150
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	295, 300–305
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	250–259
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	96
Introduction to Machine Learning with Applications in Information Securit	—	201
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	216–222
Machine learning in action	Peter Harrington	158–159
Machine Learning in Action	Peter Harrington	158–159
Machine Learning in Python	—	270–283, 297–300, 303, 312–317, 325–335, 341–347
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~263–269, ~276
Machine Learning Mastery with Python	Jason Brownlee	103–104
Machine Learning: An Algorithmic Perspective, Second Edition	—	~268–272
MachineLearningNotes	—	231–233
Master Machine Learning Algorithms - Discover how they work	—	~136–148
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	246–248, 251–257
Mastering Machine Learning with scikit-learn 2nd edition	—	156–157
Practical Machine Learning with H2O	—	197–199
Principles And Theory For Data Mining And Machine Learning	—	333–340
Pro Machine Learning Algorithms	—	130
Real-World Machine Learning	—	45–46
Statistical Machine Learning	—	Boosting

AdaBoost (12 books)

Book	Author	Pages / Concepts
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	135–142
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	296–299
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	250–253
Introduction to Machine Learning with Applications in Information Securit	—	202–204
Machine learning in action	Peter Harrington	156, 163–168
Machine Learning in Action	Peter Harrington	156, 163–168
Machine Learning in Healthcare Informatics	—	245
MachineLearningNotes	—	232–233
Master Machine Learning Algorithms - Discover how they work	—	~136–148
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	247
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	160
Pro Machine Learning Algorithms	—	139–144

Gradient Boosting (9 books)

Book	Author	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	300–305
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	254–259
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	96
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	216–222
Machine Learning in Python	—	270–283, 297–300, 303, 312–317, 325–335, 341–347
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	251–257
Practical Machine Learning with H2O	—	197–199, 203–217
Pro Machine Learning Algorithms	—	130–135, 145–146
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	541–543

XGBoost / LightGBM / CatBoost (2 books)

Book	Author	Pages / Concepts
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	254–257
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	161

Stacking & Voting (13 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	203–206, 215
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~369–370
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	131–132
grokking-deep-learning	—	~118
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	280–283, 306–309, 544–546
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	240–242, 260–262, 513–514
MachineLearningNotes	—	230
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	262–263
Mastering Machine Learning with scikit-learn 2nd edition	—	158–159
Neural Networks and Deep Learning: A Textbook	—	282–283
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	74–75
Practical Machine Learning with H2O	—	328
Principles And Theory For Data Mining And Machine Learning	—	331–332

k-Means Clustering (36 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	105–109
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~208–214
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	179–181
Essential Math for AI	Hala Nelson	k-means Clustering
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	131–134, 138–142
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	288–300
Introduction to Artificial Intelligence	—	239–240
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	88–89
Introduction to Machine Learning with Applications in Information Securit	—	155–159, 326, 333–335
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	238–241, 286–287, 290
introduction-to-algorithms-and-machine-learning	—	109–118
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	271–287
Machine learning con Python: costruire algoritmi per generare conoscenza	—	432–438
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	149–151
Machine learning in action	Peter Harrington	234–243
Machine Learning in Action	Peter Harrington	234–243
Machine Learning in Healthcare Informatics	—	80–82
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~279–291, ~307–308
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	325–330
Machine Learning with TensorFlow	—	~106–108
Machine Learning: An Algorithmic Perspective, Second Edition	—	~282–290
Machine Learning: Hands-On for Developers and Technical Professionals	—	190–191, 194
MachineLearningNotes	—	234
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	213–216
Mastering Machine Learning with scikit-learn 2nd edition	—	206–214
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	197
Practical Linear Algebra for Data Science - Mike X Cohen	—	k-Means Clustering; k-Means Exercises
Practical Machine Learning with H2O	—	291–294
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	403
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	223–225, 228–233
Principles And Theory For Data Mining And Machine Learning	—	423–425
Pro Machine Learning Algorithms	—	272–273, 278–285, 290
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	652
Python Machine Learning	—	233–254
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	126–131, 158, 465–466
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	K-means

Hierarchical Clustering (12 books)

Book	Author	Pages / Concepts
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	127–130
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	330–333
Introduction to Artificial Intelligence	—	241–242
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	350–352
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	258
Machine learning con Python: costruire algoritmi per generare conoscenza	—	458–460
MachineLearningNotes	—	242–243
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	221–222
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	287–288
Principles And Theory For Data Mining And Machine Learning	—	427–443
Pro Machine Learning Algorithms	—	288–290
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Agglomerative clustering

DBSCAN & Density Clustering (3 books)

Book	Author	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	288–290, 307–309
Machine learning con Python: costruire algoritmi per generare conoscenza	—	462–468
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Density-based clustering; DBSCAN

Spectral Clustering (2 books)

Book	Author	Pages / Concepts
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	250–254, 348–349
Principles And Theory For Data Mining And Machine Learning	—	466–471

Gaussian Mixture Models & EM (17 books)

Book	Author	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~215–233
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	93–95
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	835–843
Essential Math for AI	Hala Nelson	Gaussian Mixture Model
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	224–225
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	320
Introduction to Artificial Intelligence	—	239–240
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	90–91
Introduction to Machine Learning with Applications in Information Securit	—	178–181
Machine Learning in Healthcare Informatics	—	44
Machine Learning: An Algorithmic Perspective, Second Edition	—	~153–157
MachineLearningNotes	—	127–128, 236–239
mml-book [Reading]	—	354–355, 366–368
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	592–598, 629–630, 653
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	143–144
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	117–119
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Gaussian Mixture Model

Association Rule Mining (Apriori) (13 books)

Book	Author	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	6. Unsupervised Learning Algorithms: Clu
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~116–123, ~216–222, ~582–586
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	69, 112–118
Deep Learning. Practical Neural Networks with Java	—	330–336
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	255–259
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	95–98
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	82
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	152
Machine learning in action	Peter Harrington	251–255, 258–263, 270–271, 275, 283
Machine Learning in Action	Peter Harrington	251–255, 258–263, 270–271, 275, 283
Machine Learning in Healthcare Informatics	—	282–284
Machine Learning: Hands-On for Developers and Technical Professionals	—	143–146, 149–150, 359, 362
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	62, 410–416

Self-Organizing Maps (5 books)

Book	Author	Pages / Concepts
Deep Learning. Practical Neural Networks with Java	—	551, 557–582
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	786–788
Machine Learning with TensorFlow	—	~112–116
Neural Networks and Deep Learning: A Textbook	—	465–467
Principles And Theory For Data Mining And Machine Learning	—	566–572

Train/Test Split & Cross-Validation (34 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	248–250, 306–307
6 390 lecture notes spring24	—	23, 135
Advances in Financial Machine Learning	López de Prado	147–154, 178–182, 215–221
An Introduction to Machine Learning - Machine Learning Summer	—	Nearest Neighbor, Kernels density estima
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~253–262
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	106–108
Artificial Intelligence: With an Introduction to Machine Learning	—	109–110
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~152–153
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	149–150
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	18–19
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	111
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	114–116, 136–137
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	117–118, 135
Introduction to Artificial Intelligence	—	226–227
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	99
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~252–259
introduction-to-algorithms-and-machine-learning	—	207–220
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	246–253
Machine learning con Python: costruire algoritmi per generare conoscenza	—	253–261
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	134
Machine learning in action	Peter Harrington	102–103
Machine Learning in Action	Peter Harrington	102–103
Machine Learning in Healthcare Informatics	—	46, 84–88
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~319–323
Machine Learning Mastery with Python	Jason Brownlee	68
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	194–204, 239–247, 259, 278–281
MachineLearningNotes	—	61, 69–70
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	237–239
Neural Networks and Deep Learning: A Textbook	—	198
Practical Machine Learning with H2O	—	149–152
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	306–311, 334–335
Principles And Theory For Data Mining And Machine Learning	—	42–46, 600–611
Real-World Machine Learning	—	105–109
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	82

Bias-Variance Tradeoff (15 books)

Book	Author	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~75
introduction-to-algorithms-and-machine-learning	—	207–220
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	230–234
Machine learning in action	Peter Harrington	197–198
Machine Learning in Action	Peter Harrington	197–198
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~316–318
Machine Learning Yearning (Draft Version)	Andrew Ng	42–45, 49, 56–58
Machine Learning: An Algorithmic Perspective, Second Edition	—	~35
Master Machine Learning Algorithms - Discover how they work	—	~19–20
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	236
Mastering Machine Learning with scikit-learn 2nd edition	—	25–26
Neural Networks and Deep Learning: A Textbook	—	193
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	302–305
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	83–85
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Bias-variance decomposition of estimator; Bias variance trade-off; Bias-variance trade-off in neural networ

Overfitting & Underfitting (24 books)

Book	Author	Pages / Concepts
9.finetuning guide	—	60
Advances in Financial Machine Learning	López de Prado	220–221
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	5.4 Underfitting and Overfitting
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~91–99, ~185–189, ~225–227
grokking-deep-learning	—	~113, ~150–151
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	54–57, 458
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	58–61, 420
Introduction to Artificial Intelligence	—	226–227
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~26–28
introduction-to-algorithms-and-machine-learning	—	207–220
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	224–229
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	79
Machine learning con Python: costruire algoritmi per generare conoscenza	—	113–117, 266–268
Machine Learning for Hackers	Conway & White	181–189
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	181–189
Machine Learning in Python	—	137–141, 144–152, 255
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	197
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	26–27
MachineLearningNotes	—	18
Neural Networks and Deep Learning: A Textbook	—	45
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	305
Real-World Machine Learning	—	102–104
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	86
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Overfitting and underfitting

Regularization (L1/L2) (35 books)

Book	Author	Pages / Concepts
12.LAEF	—	Regularized Least Squares
6 390 lecture notes spring24	—	19, 62
An Introduction to Machine Learning - Machine Learning Summer	—	L1: Machine learning and probability the; L2: Density estimation and Parzen window
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	5.5 Regularization; 8.4 Advanced Regularization
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~185–210
Basics of Linear Algebra for Machine Learning	Jason Brownlee	29, 70–71
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	70–86
Essential Math for AI	Hala Nelson	Regularization Techniques; Commonly used weight decay regularizatio; Explaining the Role of the Regularizatio
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	84–87
Financial Signal Processing and Machine Learning	—	37–39
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	283–289
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	132–135
grokking-deep-learning	—	~145, ~152–153
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	199, 270–271, 458–461, 466–468
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	183, 229–231, 420–421, 427, 443
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	44, 124, 146
Language Models Interview Handbook	—	27
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	256–257
Machine learning con Python: costruire algoritmi per generare conoscenza	—	175–182
Machine Learning for Hackers	Conway & White	171–173, 185–189
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	171–173, 185–189
Machine Learning in Healthcare Informatics	—	169
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	80, 84–86, 259, 278–281
Machine Learning with TensorFlow	—	~65–68
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	72–73
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	174–176, 187–188
Mastering Machine Learning with scikit-learn 2nd edition	—	91
Neural Networks and Deep Learning: A Textbook	—	46, 200–203, 220
Practical Linear Algebra for Data Science - Mike X Cohen	—	Regularization; Regularization Exercise
Practical Machine Learning with H2O	—	253–255
Pro Machine Learning Algorithms	—	173–175
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	194, 209–210, 273, 338
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	80–81
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Regularization of neural nets
UnderstandingDeepLearning 02 09 26 C [Reading]	—	152–156

Dropout (8 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	62
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~211–214
Deep Learning. Practical Neural Networks with Java	—	119–131
Essential Math for AI	Hala Nelson	Dropout
grokking-deep-learning	—	~153–157
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	462–465, 607–608
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	422–426
Neural Networks and Deep Learning: A Textbook	—	207–209

Confusion Matrix & Precision/Recall/F1 (22 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	661–666, 792, 831–833, 837–851
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~239–244
Artificial Intelligence: With an Introduction to Machine Learning	—	254–255
Demystifying Big Data and Machine Learning for Healthcare	—	138–141, 166–167
Essential Math for AI	Hala Nelson	Sensitivity
Financial Signal Processing and Machine Learning	—	157, 169–183
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	138–147
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	138–142, 234
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	82
Machine learning con Python: costruire algoritmi per generare conoscenza	—	277–278
Machine learning in action	Peter Harrington	170–173
Machine Learning in Action	Peter Harrington	170–173
Machine Learning in Healthcare Informatics	—	323
Machine Learning with TensorFlow 1x	—	228–229
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	39–43
Machine-Learning-Systems	—	643–648, 774, 813–814, 819–833
Mastering Machine Learning with scikit-learn 2nd edition	—	110–111
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	290–291
Pro Machine Learning Algorithms	—	23
Real-World Machine Learning	—	112
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	218, 250–251
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Sensitivity of neural networks to small ; F-measure

ROC, AUC & Thresholds (15 books)

Book	Author	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	148–151
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	143–146
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	81
Introduction to Machine Learning with Applications in Information Securit	—	244–246
Machine learning con Python: costruire algoritmi per generare conoscenza	—	279–282
Machine learning in action	Peter Harrington	170–173
Machine Learning in Action	Peter Harrington	170–173
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	44–45
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	184
Mastering Machine Learning with scikit-learn 2nd edition	—	112–113
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI	—	184
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	294–295
Pro Machine Learning Algorithms	—	24–27, 127–128
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	575–576
Real-World Machine Learning	—	113–115

Hyperparameter Tuning (39 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	230, 239–240, 297–302, 344–347
13.Machine-Learning-Systems	—	1084, 2425–2452
2.Foundation of LLM	—	49–52, 164–173
3.Reinforcement Learning- An Overview	Kevin Murphy	17
6 390 lecture notes spring24	—	23, 136
9.finetuning guide	—	10–17, 22–23, 29, 36–39, 45–49, 61, 77–80, 86–87, 90–93, 96–102
Advances in Financial Machine Learning	López de Prado	178–179, 183–185
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	5.7 Hyperparameter Tuning
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~271–274, ~277–284, ~289–309, ~312–320
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	96
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	367–380
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~574–577
Essential Math for AI	Hala Nelson	Explaining the Role of the Learning Rate; Explaining the Role of the Regularizatio; Hyperparameter Examples That Appear in M
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	84–87
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	19–20, 105–127, 175–186
grokking-deep-learning	—	~279
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	118–120, 270–271, 336–338, 409
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	62, 119–120, 229–231, 372–376, 379–380, 800–803
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	99, 121–123, 132
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~260–274, ~309–311
Language Models Interview Handbook	—	47, 91, 95–103, 124
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~199–201
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	256–257
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	303–306
Machine Learning Mastery with Python	Jason Brownlee	107–108, 147, 166–168
Machine-Learning-Systems	—	1065
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	227–230, 246, 253, 264
Mastering Machine Learning with scikit-learn 2nd edition	—	114–115
mml-book [Reading]	—	289–294
Neural Networks and Deep Learning: A Textbook	—	145, 197, 206
Practical Linear Algebra for Data Science - Mike X Cohen	—	Grid Search to Find Model Parameters; Grid Search Exercises
Practical Machine Learning with H2O	—	172
Practical Machine Learning with Python	—	~255, ~282–294
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	72, 273, 300–301, 309–311, 342
Principles And Theory For Data Mining And Machine Learning	—	144, 304
Real-World Machine Learning	—	123–126
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	139–141, 467
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	130
UnderstandingDeepLearning 02 09 26 C [Reading]	—	105–109, 146

Learning Curves (7 books)

Book	Author	Pages / Concepts
10.marl	—	~36–38
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	27
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	159–160, 168
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	193–198
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	179–182
Machine Learning Yearning (Draft Version)	Andrew Ng	60–65
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	71

Multilayer Perceptron / Feedforward (37 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	152, 175–179
10.marl	—	~165–168
13.Machine-Learning-Systems	—	182
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~83
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	24–30
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	295–299
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	746–755
Artificial Intelligence for Business: What You Need to Know about Machine Learning and Neural Networks	—	132–155
Artificial Intelligence: With an Introduction to Machine Learning	—	410–412, 416–417
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python	—	8–13
Deep Learning. Practical Neural Networks with Java	—	468–475
Demystifying Big Data and Machine Learning for Healthcare	—	114–115
Essential Math for AI	Hala Nelson	The Brain Cortex and Artificial Neural N; Training Function: Fully Connected, or D; Artificial Neural Networks
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	160–161
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	258–262
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	383, 399–400, 728–730
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	327, 337–340, 356
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	20, 102, 107–111, 119
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	216–218
Language Models Interview Handbook	—	36
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	71–72
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	154–155
Machine Learning in Healthcare Informatics	—	304
Machine Learning: An Algorithmic Perspective, Second Edition	—	~89–100
Machine Learning: Hands-On for Developers and Technical Professionals	—	117–130
Machine-Learning-Systems	—	166
MachineLearningNotes	—	193–199
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	315, 320–323, 329–331
Mastering Machine Learning with scikit-learn 2nd edition	—	190–192
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	210
Neural Networks and Deep Learning: A Textbook	—	344
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	120–127
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	52–53, 122–123
Pro Machine Learning Algorithms	—	148, 217–218
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	314, 318
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	188–191, 277–278
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Feed Forward neural network

Forward Propagation (6 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	205–208, 225–228, 646–648
9.finetuning guide	—	68
grokking-deep-learning	—	~21, ~220, ~224
Machine-Learning-Systems	—	188–191, 209–212, 629–630
Pro Machine Learning Algorithms	—	151–153
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	326–329

Backpropagation (31 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	141, 151, 173–174
12.LAEF	—	Chains & Backpropagation
13.Machine-Learning-Systems	—	212–215, 634–636, 649
6 390 lecture notes spring24	—	55, 58, 69, 127–130
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	40
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	32–44, 70–86
Deep Learning. Practical Neural Networks with Java	—	544–546
Essential Math for AI	Hala Nelson	Chain Rule and Backpropagation: Calculat; Backpropagation Is Not Too Different fro; Backpropagation in Detail
grokking-deep-learning	—	~99, ~119–120, ~126–129, ~225, ~240, ~267–270, ~273
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	395–398
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	337–340
Introduction to Artificial Intelligence	—	281–283, 298
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	108–109, 127
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	216–217
introduction-to-algorithms-and-machine-learning	—	343–372
Language Models Interview Handbook	—	107, 110–111
Machine Learning in Healthcare Informatics	—	45
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	259, 269
Machine Learning: An Algorithmic Perspective, Second Edition	—	~74–84, ~101–107
Machine Learning: Hands-On for Developers and Technical Professionals	—	124
Machine-Learning-Systems	—	195–198, 616–618, 631–632
Mastering Machine Learning with scikit-learn 2nd edition	—	196–200
mml-book [Reading]	—	165–169
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	211–212
Neural Networks and Deep Learning: A Textbook	—	41–43, 127–130, 133–140, 143, 298–300, 351–353
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	53
Principles And Theory For Data Mining And Machine Learning	—	207–211
Pro Machine Learning Algorithms	—	159–160
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	330–332
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Backpropagation algorithm
UnderstandingDeepLearning 02 09 26 C [Reading]	—	117–120

Activation Functions (Sigmoid/Tanh/ReLU) (24 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	32, 49, 153–154, 175–179, 270–276, 323–332
6 390 lecture notes spring24	—	53–55
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	7. Deep Learning and Neural Networks: Ar
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	38–39
Artificial Intelligence: With an Introduction to Machine Learning	—	418–419
Essential Math for AI	Hala Nelson	Pass the result through a nonlinear acti; Common Activation Functions
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	166
grokking-deep-learning	—	~161–167
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	413, 422–425
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	389–394, 443
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	119
Language Models Interview Handbook	—	109
Machine learning in action	Peter Harrington	111–112
Machine Learning in Action	Peter Harrington	111–112
Machine Learning: Hands-On for Developers and Technical Professionals	—	121
MachineLearningNotes	—	191
Mastering Machine Learning with scikit-learn 2nd edition	—	162–163
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	209
Neural Networks and Deep Learning: A Textbook	—	36, 153, 342
Practical Machine Learning with H2O	—	250–252
Pro Machine Learning Algorithms	—	66–68, 154–158
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	319
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	192
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	195–196, 200–201

Softmax (12 books)

Book	Author	Pages / Concepts
3.Reinforcement Learning- An Overview	Kevin Murphy	64–65
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~90
Essential Math for AI	Hala Nelson	Softmax Regression: Classify into Multip
grokking-deep-learning	—	~169
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	217–221
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	198–200
Language Models Interview Handbook	—	106, 123
Machine learning con Python: costruire algoritmi per generare conoscenza	—	555
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	105–107, 118, 133–137, 239
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	210
Neural Networks and Deep Learning: A Textbook	—	88–89, 137
Pro Machine Learning Algorithms	—	166–167

Weight Initialization (Xavier/He) (4 books)

Book	Author	Pages / Concepts
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~125–126
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	420–421
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	387–388
UnderstandingDeepLearning 02 09 26 C [Reading]	—	121–124

Loss / Cost Functions (20 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	209–211
6 390 lecture notes spring24	—	39, 55
9.finetuning guide	—	30
Advances in Financial Machine Learning	López de Prado	416
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	40
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	60–62
Essential Math for AI	Hala Nelson	Loss Function; For linear regression, the loss function; Minimizing the mean squared error loss f (+1)
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	418, 423
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	212–213
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	193–194, 440
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	53, 363–368
Machine learning in action	Peter Harrington	174
Machine Learning in Action	Peter Harrington	174
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	70–72, 84–86, 102, 105–107, 114–115, 261–262, 270–272, 283–285
Machine-Learning-Systems	—	192–194
Mastering Machine Learning with scikit-learn 2nd edition	—	37–38
Neural Networks and Deep Learning: A Textbook	—	28–35, 141–142
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	528–529
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	226
UnderstandingDeepLearning 02 09 26 C [Reading]	—	70–74, 259–261, 375–376, 421–424

Batch & Layer Normalization (7 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	63, 121–123
Essential Math for AI	Hala Nelson	Batch Normalization of Each Layer
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	426–431
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	395–399
Language Models Interview Handbook	—	36
Neural Networks and Deep Learning: A Textbook	—	172–175, 307
UnderstandingDeepLearning 02 09 26 C [Reading]	—	206–208

Vanishing/Exploding Gradients (10 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	131
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	38–39
grokking-deep-learning	—	~272
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	418–419, 432
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	386, 400
Language Models Interview Handbook	—	110–111
MachineLearningNotes	—	201–207
Neural Networks and Deep Learning: A Textbook	—	48, 149, 162
Pro Machine Learning Algorithms	—	255–257
UnderstandingDeepLearning 02 09 26 C [Reading]	—	206–208

Epochs, Batches & Training Loops (7 books)

Book	Author	Pages / Concepts
9.finetuning guide	—	31
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	188–189
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	176, 379–380, 458–460
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	—	68
Neural Networks and Deep Learning: A Textbook	—	141–142
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	325
UnderstandingDeepLearning 02 09 26 C [Reading]	—	301–302

Convolution & Filters (41 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	251–257, 308–312
10.marl	—	~175–179
6 390 lecture notes spring24	—	64–66
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	9. Computer Vision and Image Recognition
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	133–134
An Introduction to Machine Learning - Machine Learning Summer	—	Hebb's rule, perceptron algorithm, conve
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~323–341
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	603–608
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	69
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python	—	14–40
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	87–90
Deep Learning. Practical Neural Networks with Java	—	119, 132–153
Essential Math for AI	Hala Nelson	5. Convolutional Neural Networks and Com; Convolution and Cross-Correlation; Convolution in Usual Space Is a Product (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	162–165
grokking-deep-learning	—	~177–180
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	536, 539–546, 736–739
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	507–517, 553–554, 676, 693–695
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	283–287
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	21, 114–117, 170, 215–216
introduction-to-algorithms-and-machine-learning	—	421–423
Language Models Interview Handbook	—	64
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	94
Machine Learning in Healthcare Informatics	—	93–95
Machine Learning with TensorFlow	—	~169–172, ~182–188
Machine Learning with TensorFlow 1x	—	82–90
Machine Learning: An Algorithmic Perspective, Second Edition	—	~291–299
MachineLearningNotes	—	211
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	337
Mastering Machine Learning with scikit-learn 2nd edition	—	78–79
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	207–209
Neural Networks and Deep Learning: A Textbook	—	60–61, 332–338, 349–355, 374–380
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	37–39, 81–84, 100
Practical Machine Learning with Python	—	~499–500
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	53, 514–515
Pro Machine Learning Algorithms	—	191, 199–205, 215–217
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	229–230, 248–249, 689, 695, 712, 715–721
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	110
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	41, 239
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	76–83, 88–100, 103–109, 146, 161, 170–173, 178, 183–186, 242–251
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Convolution properties; Convolution with separable kernels; Application of Gaussian filter (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	175–184, 262–264, 270–273

Pooling (Max/Average) (13 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	67, 70
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~342–345
Essential Math for AI	Hala Nelson	Pooling
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	169–172
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	552–553
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	519–522
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	118
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	325–328
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	210
Neural Networks and Deep Learning: A Textbook	—	205, 343
Pro Machine Learning Algorithms	—	201–205, 217
Real-World Machine Learning	—	222–223
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Pooling layer

Padding & Stride (1 books)

Book	Author	Pages / Concepts
Neural Networks and Deep Learning: A Textbook	—	339–340

CNN Architectures (LeNet/AlexNet/VGG/ResNet) (6 books)

Book	Author	Pages / Concepts
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	169–172
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	556–571
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	526–536, 543
Machine Learning with TensorFlow 1x	—	75–77, 176
Neural Networks and Deep Learning: A Textbook	—	356–366
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	AlexNet; Inception; VGG (+1)

Convolutional Neural Networks (general) (22 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	222–229, 233–238, 360–361, 377
6 390 lecture notes spring24	—	64, 69
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	9. Computer Vision and Image Recognition
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~346–354
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python	—	30–40
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	87–90
Deep Learning. Practical Neural Networks with Java	—	119, 132–153
Essential Math for AI	Hala Nelson	5. Convolutional Neural Networks and Com; A Convolutional Neural Network for Image; Convolutional Neural Networks for Time S
grokking-deep-learning	—	~177
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	536, 554–555, 736–739
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	507, 523–525, 542–543
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	21, 114–117, 121–123, 215–216
Machine Learning with TensorFlow	—	~169–172, ~182–188
MachineLearningNotes	—	211
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	337–347
Mastering Machine Learning with scikit-learn 2nd edition	—	78–79
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	207
Neural Networks and Deep Learning: A Textbook	—	60–61, 332–334
Practical Machine Learning with Python	—	~499–500
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	53, 514–523
Pro Machine Learning Algorithms	—	191, 199–200, 206–214, 219–221, 226
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Development of CNN; Application of CNN models; R-CNN – Regions with CNN features

Transfer Learning (14 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	230, 239–240
13.Machine-Learning-Systems	—	2321–2325
2.Foundation of LLM	—	10–13
9.finetuning guide	—	9, 58, 66
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	8.7 Transfer Learning
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	117, 409, 433–434
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	119–120, 372–376, 401–404, 501, 544–548, 621–622
Language Models Interview Handbook	—	101
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	312
Machine Learning with TensorFlow 1x	—	187–192, 218–224
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	353–356
Neural Networks and Deep Learning: A Textbook	—	62–63, 368
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	520–523
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	96–112

Object Detection (YOLO/R-CNN/SSD) (8 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	377
13.Machine-Learning-Systems	—	1985–2004, 2113–2131, 2197–2218, 2245–2246, 2317–2371, 2425–2452
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	9. Computer Vision and Image Recognition
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	551–552
Machine Learning with TensorFlow 1x	—	263
Neural Networks and Deep Learning: A Textbook	—	382
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	249
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Object detection; R-CNN – Regions with CNN features; YOLO – You Only Look Once

Image Segmentation (12 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	377
13.Machine-Learning-Systems	—	2425–2452
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	62
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	39, 47–48, 453–455
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	301–302, 559–562
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	87
Machine Learning in Healthcare Informatics	—	242, 311
Machine Learning with TensorFlow	—	~109–111
Practical Machine Learning with Python	—	~373, ~378–391
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	390, 395–396
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	222, 231–233
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Image segmentation; U-Net

Image Classification & Recognition (15 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	377
13.Machine-Learning-Systems	—	232, 1959–2004, 2083–2108, 2113–2131, 2197–2244, 2273–2294, 2302–2313
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	9. Computer Vision and Image Recognition
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	146–147
Artificial Intelligence: With an Introduction to Machine Learning	—	422–427
Deep Learning. Practical Neural Networks with Java	—	387–404
Essential Math for AI	Hala Nelson	A Convolutional Neural Network for Image
Handbook of Statistics: Machine Learning: Theory and Applications	—	~249–268
Machine Learning with TensorFlow 1x	—	266
Machine-Learning-Systems	—	216
Mastering Machine Learning with scikit-learn 2nd edition	—	240–242
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	120–135, 167
Practical Machine Learning with Python	—	~501–508
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	516
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Image classification

Recurrent Neural Networks (RNN) (20 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
10.marl	—	~175–179
6 390 lecture notes spring24	—	124–133
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~323–324
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	24–25, 41–50
Deep Learning. Practical Neural Networks with Java	—	188
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python	—	45–58, 89–114
Essential Math for AI	Hala Nelson	Recurrent Neural Networks for Time Serie; How Do Recurrent Neural Networks Work?
grokking-deep-learning	—	~260–262, ~273
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	575, 602, 605–606, 740–741
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	584–586, 606, 609, 612–614
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	21, 126, 133–136
Language Models Interview Handbook	—	43
Machine Learning with TensorFlow	—	~189–194, ~198
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	349
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	212
Neural Networks and Deep Learning: A Textbook	—	58–59, 289–295, 301–306, 315
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	54, 501
Pro Machine Learning Algorithms	—	228–234, 238, 241–254, 267
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Recurrent neural networks; Training RNN; Stacked LSTM/RNN (+1)

LSTM (15 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
6 390 lecture notes spring24	—	132–133
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	41–42, 51
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python	—	89–114
Essential Math for AI	Hala Nelson	Gated Recurrent Units and Long Short-Ter
grokking-deep-learning	—	~265, ~274–279
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	611–614
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	129–131
MachineLearningNotes	—	212–213
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	350–352
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	213
Neural Networks and Deep Learning: A Textbook	—	310–312
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	55, 501
Pro Machine Learning Algorithms	—	256–266
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Long Short-Term Memory (LSTM); Stacked LSTM/RNN

GRU (6 books)

Book	Author	Pages / Concepts
6 390 lecture notes spring24	—	132–133
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python	—	89–114
Essential Math for AI	Hala Nelson	Gated Recurrent Units and Long Short-Ter
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	616–617
Neural Networks and Deep Learning: A Textbook	—	313–314
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Gated Recurrent Unit (GRU)

Bidirectional & Deep RNNs (6 books)

Book	Author	Pages / Concepts
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	135–136
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	603–606
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	585–586, 629–630
Language Models Interview Handbook	—	37
Machine learning in bioinformatics	—	321–338
Neural Networks and Deep Learning: A Textbook	—	301

Sequence-to-Sequence / Encoder-Decoder (13 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
2.Foundation of LLM	—	22–26
6 390 lecture notes spring24	—	127–130
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	7.7 Sequence-to-Sequence Learning
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	142
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	621–624
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	590–592, 623–628
Language Models Interview Handbook	—	42–43
Machine Learning with TensorFlow	—	~201, ~205–209
Machine Learning with TensorFlow 1x	—	104–105
Neural Networks and Deep Learning: A Textbook	—	317–318
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Sequence to Sequence; Encoder-Decoder architecture
UnderstandingDeepLearning 02 09 26 C [Reading]	—	240

Attention Mechanism (12 books)

Book	Author	Pages / Concepts
12.LAEF	—	Attention & Transformers
13.Machine-Learning-Systems	—	282–292
6 390 lecture notes spring24	—	73–74
big llm book	—	~1–11
Essential Math for AI	Hala Nelson	Transformers and Attention Models; The Attention Mechanism
Explainable and Interpretable Models in Computer Vision and Machine Learning	—	~173–196
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	605, 632–647
Language Models Interview Handbook	—	31–34, 37, 106
Machine-Learning-Systems	—	265–276
Neural Networks and Deep Learning: A Textbook	—	436–443
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Attention mechanism; Key-value-query formulation of attention; Self-attention and transformers (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	222–228

Transformers (17 books)

Book	Author	Pages / Concepts
12.LAEF	—	Attention & Transformers
13.Machine-Learning-Systems	—	288–292
2.Foundation of LLM	—	45–46
6 390 lecture notes spring24	—	71–72, 75
9.finetuning guide	—	81–82
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	90–93
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	345–353
big llm book	—	~1–2
Essential Math for AI	Hala Nelson	Transformers and Attention Models; The Transformer Architecture; Transformers Are Far from Perfect
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	107
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	107–110, 637–660
Language Models Interview Handbook	—	31–37, 43
LLM Interview	—	8–10
Machine-Learning-Systems	—	270–276
MachineLearningNotes	—	214–220
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Self-attention and transformers; Transformer architecture
UnderstandingDeepLearning 02 09 26 C [Reading]	—	221, 229–232, 241–245

Positional Encoding (3 books)

Book	Author	Pages / Concepts
big llm book	—	~12–18
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	153–154
Language Models Interview Handbook	—	35

Time Series Forecasting (19 books)

Book	Author	Pages / Concepts
Advances in Financial Machine Learning	López de Prado	427
Artificial Intelligence: With an Introduction to Machine Learning	—	341–345
Deep Learning. Practical Neural Networks with Java	—	584–585
Essential Math for AI	Hala Nelson	7. Natural Language and Finance AI: Vect; Convolutional Neural Networks for Time S; Recurrent Neural Networks for Time Serie (+1)
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	596–601
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	571–576, 583–592
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	122, 206–207
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	113–123
Machine learning in action	Peter Harrington	23, 178–179, 199
Machine Learning in Action	Peter Harrington	23, 178–179, 199
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	203–205
Neural Networks and Deep Learning: A Textbook	—	323–324
Practical Linear Algebra for Data Science - Mike X Cohen	—	Time Series Filtering and Feature Detect
Practical Machine Learning with Python	—	~467–482
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	137, 483–486, 490–491
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	376–377, 383–389, 418–425
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	88–90, 257
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	178, 183–186, 207, 234–235, 239–241
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Time series models; Decomposition of time series; Time series forecasting

Tokenization & Text Preprocessing (18 books)

Book	Author	Pages / Concepts
9.finetuning guide	—	96
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	55–60
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	63, 152–155, 159
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	57–58, 62–63, 74
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	61–74
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	31, 35–38, 41–46, 398, 484, 644
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	499–500
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	248
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~334–335, ~339–346
Language Models Interview Handbook	—	16–17, 21
LLM Interview	—	4–5, 19–22
Machine learning in action	Peter Harrington	101
Machine Learning in Action	Peter Harrington	101
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	277–285
Mastering Machine Learning with scikit-learn 2nd edition	—	65–68
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	42, 65, 72–78, 84, 93–94, 163–164, 224
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	134, 231
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Text preprocessing; WordPiece tokenization

Bag of Words & TF-IDF (13 books)

Book	Author	Pages / Concepts
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	61, 68–72
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	82–84
Essential Math for AI	Hala Nelson	Term Frequency Vector Representation of ; Term Frequency-Inverse Document Frequenc
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	77–78, 81
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	246–247
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~327–333, ~336–337
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	286–289
Mastering Machine Learning with scikit-learn 2nd edition	—	69–70
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	100, 110–113, 133, 156–159, 195–196
Neural Networks and Deep Learning: A Textbook	—	107–109
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	230–232, 252–254
Real-World Machine Learning	—	207
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Bag of Words (BoW) model; Term Frequency (TF)-Inverted Document Fr

Word Embeddings (word2vec/GloVe) (14 books)

Book	Author	Pages / Concepts
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	21, 87–90, 100–112, 115–124
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	85–87
Essential Math for AI	Hala Nelson	Word2vec vector representation of indivi; Facebook’s fastText vector representatio; Addressing Bias in Word Vectors
grokking-deep-learning	—	~199, ~212, ~216
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	619–620
Machine learning in action	Peter Harrington	94–97
Machine Learning in Action	Peter Harrington	94–97
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	307–308
Mastering Machine Learning with scikit-learn 2nd edition	—	73–75
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	103–104, 114, 217
Neural Networks and Deep Learning: A Textbook	—	107–109, 115–117
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	236–238
Pro Machine Learning Algorithms	—	179–189
Real-World Machine Learning	—	213–214

Language Models (13 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2375–2381, 2419–2422
6 390 lecture notes spring24	—	127–130
9.finetuning guide	—	8–9, 88, 91
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	22, 130
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	159, 294
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	879–883
Essential Math for AI	Hala Nelson	Probabilistic Language Modeling; Language Models
grokking-deep-learning	—	~266, ~277–279
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	369–371, 437
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	501, 621–622
Language Models Interview Handbook	—	19, 40–46, 59–61, 81–82, 104–107, 122
Neural Networks and Deep Learning: A Textbook	—	295
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Neural language models

Named Entity Recognition & POS Tagging (8 books)

Book	Author	Pages / Concepts
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	64
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	52–56
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	57
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	231–236, 251–252, 342, 454, 468, 636–641, 645
Language Models Interview Handbook	—	63
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	280
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	125
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	135

Sentiment Analysis & Text Classification (29 books)

Book	Author	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	8. Natural Language Processing: Text Ana
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	21, 64–67
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	102–105, 304–305
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	884–885
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~578–581
Deep Learning. Practical Neural Networks with Java	—	426–428, 444–448
Essential Math for AI	Hala Nelson	Sentiment Analysis; Spam Filter
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	653–666, 670, 676–677
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	615–617
Introduction to Artificial Intelligence	—	233–234
Introduction to Machine Learning with Applications in Information Securit	—	304–307, 317–318
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~325–326
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	130–131
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	94–95
Machine learning con Python: costruire algoritmi per generare conoscenza	—	328–329
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	90
Machine Learning for Hackers	Conway & White	89–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	89–100
Machine learning in action	Peter Harrington	92–93
Machine Learning in Action	Peter Harrington	92–93
Machine Learning: Hands-On for Developers and Technical Professionals	—	241–246, 357–358
MachineLearningNotes	—	131–132
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	302–304
Mastering Machine Learning with scikit-learn 2nd edition	—	106, 173
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	140–141, 160, 169–171
Practical Machine Learning with Python	—	~331, ~345, ~363–371
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	135, 348, 362, 380–384
Pro Machine Learning Algorithms	—	266
Real-World Machine Learning	—	195

Topic Modeling (13 books)

Book	Author	Pages / Concepts
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	67–68
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	21, 87–95
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	131–138
Deep Learning. Practical Neural Networks with Java	—	426–428, 437–443
Essential Math for AI	Hala Nelson	Topic Vector Representation of a Documen
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~347–354
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	312–313, 374–379
Language Models Interview Handbook	—	52–53, 57–58
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	300–301
Neural Networks and Deep Learning: A Textbook	—	278–279
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	235, 385–388
Real-World Machine Learning	—	172–174
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Topic models; Probabilistic generative models: Latent

Machine Translation (8 books)

Book	Author	Pages / Concepts
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	130
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	926–930
Essential Math for AI	Hala Nelson	Machine Translation
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	346, 435, 441, 451, 473
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	621–624
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	623–628
Neural Networks and Deep Learning: A Textbook	—	317–318, 440–443
UnderstandingDeepLearning 02 09 26 C [Reading]	—	240

Chatbots & Dialogue (9 books)

Book	Author	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	8. Natural Language Processing: Text Ana
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	227
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	16–24, 27–28, 31, 35–41, 44–45, 52–56, 68, 73, 77–80, 96–100, 118, 122–124, 129, 140–145, 149–152, 168–170, 176–179, 182–187, 191–199
Essential Math for AI	Hala Nelson	Chatbots
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	511–512, 527–529
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	94–95
Machine Learning with TensorFlow	—	~201
Machine Learning with TensorFlow 1x	—	266
Neural Networks and Deep Learning: A Textbook	—	423–425

Autoencoders (18 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
6 390 lecture notes spring24	—	110–112
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	87–90
Deep Learning. Practical Neural Networks with Java	—	80
Essential Math for AI	Hala Nelson	Explicit Density-Intractable: Variationa; The Original Autoencoder
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	627, 630–632, 637–639, 644–646, 649–651, 654–657, 660, 742–744
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	663–664, 667–669, 672–685
Introduction to Artificial Intelligence	—	291
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	138–139, 208–214
Machine Learning with TensorFlow	—	~135, ~140–144, ~150
MachineLearningNotes	—	209–210
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	332–336
Neural Networks and Deep Learning: A Textbook	—	90–93, 98–99, 221–228, 231, 374–379, 457–458
Practical Machine Learning with H2O	—	295–298, 344
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	55
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	297–299
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Autoencoder; Iris autoencoder; Variational Autoencoders (VAE)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	341, 350–352

Variational Autoencoders (VAE) (7 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
Essential Math for AI	Hala Nelson	Explicit Density-Intractable: Variationa
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	654–657
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	682–685
Neural Networks and Deep Learning: A Textbook	—	226–228, 231, 457–458
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Variational Autoencoders (VAE)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	341, 350–352

Generative Adversarial Networks (GAN) (7 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
Essential Math for AI	Hala Nelson	Implicit Density-Direct: Generative Adve; How Do Generative Adversarial Networks W
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	663–664, 687–698
MachineLearningNotes	—	214–220
Neural Networks and Deep Learning: A Textbook	—	65, 232, 453–463
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Generative Adversarial Nets; Equilibrium state for GAN training; Implementing GAN (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	290–294

GAN Variants (DCGAN/CycleGAN/StyleGAN) (3 books)

Book	Author	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	699–700
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Wasserstein GAN (WGAN); WGAN training; Conditional GAN (cGAN) (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	310–311

Diffusion Models (3 books)

Book	Author	Pages / Concepts
Artificial Intelligence: With an Introduction to Machine Learning	—	291–297
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	663–664, 701–708
UnderstandingDeepLearning 02 09 26 C [Reading]	—	363

Boltzmann Machines & RBMs (9 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	61–66, 188
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	87–90
Deep Learning. Practical Neural Networks with Java	—	80
Essential Math for AI	Hala Nelson	Boltzmann Machine; Restricted Boltzmann Machine (Explicit D
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	778–785
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	22, 138–141, 147
Machine Learning: An Algorithmic Perspective, Second Edition	—	~359, ~369–384
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	324–328
Neural Networks and Deep Learning: A Textbook	—	58–59, 253, 261–269, 282–285

Transformer-based LLMs (GPT/BERT) (7 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	1408, 2375–2378, 2382–2409
2.Foundation of LLM	—	28–33, 37–41, 162–163
9.finetuning guide	—	8–11, 16, 23, 55, 58, 63, 66–70, 75–76, 82, 88
Language Models Interview Handbook	—	18, 23, 45–51, 59–61, 74–75, 81–83, 109, 118, 123–125
Machine-Learning-Systems	—	1388
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	BERT; Pre-training BERT; Input representation for pre-training ta
UnderstandingDeepLearning 02 09 26 C [Reading]	—	233–235

Pretraining & Fine-tuning (15 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	230, 239–240
13.Machine-Learning-Systems	—	2321–2325, 2425–2452
2.Foundation of LLM	—	8–27, 49–52, 164–173
9.finetuning guide	—	9–17, 22–23, 36–41, 45–48, 58, 62–63, 66, 73, 77–80, 86–87, 90–93, 96–102
Deep Learning. Practical Neural Networks with Java	—	119
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	117, 409, 433–434, 442–444, 644–645, 757
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	119–120, 372–376, 401–402, 405–406, 501, 544–548, 621–622, 672, 810
Language Models Interview Handbook	—	38–39, 47, 91, 95–103
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	312
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	256–257
Machine Learning with TensorFlow 1x	—	187–192, 216–224
Neural Networks and Deep Learning: A Textbook	—	62–63, 212–217, 368
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	520–523
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	297–299
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Pre-training BERT; Input representation for pre-training ta; Generative Pre-Training by OpenAI

Prompt Engineering & In-Context Learning (7 books)

Book	Author	Pages / Concepts
11.context-engineering	—	13
13.Machine-Learning-Systems	—	1299–1300, 2425–2452
2.Foundation of LLM	—	58–62, 103–112, 122–123, 145–159
9.finetuning guide	—	12, 74
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	Andriy Burkov	7.11 Zero-Shot Learning
Language Models Interview Handbook	—	47–48, 74–80, 85
Machine-Learning-Systems	—	1279

Retrieval-Augmented Generation (RAG) (23 books)

Book	Author	Pages / Concepts
11.context-engineering	—	27–29
13.Machine-Learning-Systems	—	1624–1625, 2382–2409
2.Foundation of LLM	—	141–144
6 390 lecture notes spring24	—	71–72, 76
9.finetuning guide	—	13–14
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	21, 87–90, 104–112, 115, 121–124
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	153–154
Essential Math for AI	Hala Nelson	Meaning Vector Representations of Words
grokking-deep-learning	—	~194–195, ~199, ~213–216, ~255–257
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	388
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	619–620
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	494–498, 621–622
Language Models Interview Handbook	—	24–30, 35, 54, 62–64, 67–73, 88–94, 107
LLM Interview	—	6–7
Machine learning con Python: costruire algoritmi per generare conoscenza	—	353–354
Machine Learning with TensorFlow	—	~231–233
Machine Learning: An Algorithmic Perspective, Second Edition	—	~144–146
Machine-Learning-Systems	—	1604–1605
Mastering Machine Learning with scikit-learn 2nd edition	—	73–75
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	103–104
Neural Networks and Deep Learning: A Textbook	—	107–114, 118–119
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	236–238
Pro Machine Learning Algorithms	—	249–253

RLHF & Alignment (11 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	1637–1639, 1845–1846
2.Foundation of LLM	—	162–167, 179, 186–188, 194–199, 205–207
3.Reinforcement Learning- An Overview	Kevin Murphy	110
9.finetuning guide	—	54–55
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	393–398, 404–417, 420, 427, 438
Introduction to Machine Learning with Applications in Information Securit	—	61–68
Language Models Interview Handbook	—	102
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~245
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	257–259, 271
Machine-Learning-Systems	—	1617–1619, 1854–1855
UnderstandingDeepLearning 02 09 26 C [Reading]	—	435–440

Agents & Tool Use (16 books)

Book	Author	Pages / Concepts
10.marl	—	~2–8, ~17–18, ~43, ~89, ~95–101, ~109–111, ~115, ~127–139, ~159–160, ~219, ~222, ~230–241, ~266–280, ~305–306, ~319, ~337–340
11.context-engineering	—	28, 33–34, 37–43, 55
13.Machine-Learning-Systems	—	364, 1314–1317, 1847–1849, 2382–2409
2.Foundation of LLM	—	141–144
6.openAI guide to building practical agents	—	~4–23
9.finetuning guide	—	51
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	53–54, 65–77, 83–87, 166–171, 253–254, 284–292, 685–697, 1063–1067
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	149–152
Demystifying Big Data and Machine Learning for Healthcare	—	125–126
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	331–333
Essential Math for AI	Hala Nelson	An AI Agent’s Specific Tasks; Codifying Logic Within an Agent; Chemical Warfare Agents
Introduction to Artificial Intelligence	—	25, 32–33
Language Models Interview Handbook	—	71, 74, 78
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	67
Machine-Learning-Systems	—	346, 1294–1297, 1822–1823, 1849–1851, 1856–1858
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	104–112

Inference: KV Cache, Quantization, Decoding (33 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	123, 220, 755, 831, 852, 1092, 1105–1119, 1681–1682, 2005–2022, 2029–2046, 2109–2112, 2135–2166, 2169–2194, 2321–2357, 2367–2371
2.Foundation of LLM	—	207
3.Reinforcement Learning- An Overview	Kevin Murphy	25, 29–30, 110
7.pen and paper exercise in ML	—	119, 156–160, 189–192, 199–204
9.finetuning guide	—	67
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~139
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	27–28, 65–66
An Introduction to Machine Learning - Machine Learning Summer	—	Introduction to pattern recognition, cla
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	227–232, 341–343, 509–512, 541–557, 589–596
Artificial Intelligence: With an Introduction to Machine Learning	—	175–179, 300
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	229–231
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	26–30
Financial Signal Processing and Machine Learning	—	193–198
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	269, 372–374, 388
Introduction to Artificial Intelligence	—	149–153
Introduction to Machine Learning with Applications in Information Securit	—	221
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	37–56, 174–175, 293–295, 371–372, 403–407, 430, 434–443, 448–451, 463, 568–576, 585–590
Language Models Interview Handbook	—	16–17, 20, 93, 112–116
LLM Interview	—	11–12, 17–18
Machine Learning in Healthcare Informatics	—	83, 123
Machine Learning with TensorFlow	—	~128–129
Machine-Learning-Systems	—	107, 204, 737, 813, 834, 1073–1074, 1087–1100, 1662–1664
MachineLearningNotes	—	107–111, 119
Master Machine Learning Algorithms - Discover how they work	—	~106–114
Mastering Machine Learning with scikit-learn 2nd edition	—	221–222
mml-book [Reading]	—	278–283
Neural Networks and Deep Learning: A Textbook	—	465–467
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	137
Principles And Theory For Data Mining And Machine Learning	—	192–194, 207–211
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	45, 130–135, 554–555
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	251–252
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	122, 125, 212, 286–287
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	5. Statistical Inference and Application; Statistical inference; Sampling and quantization

Markov Decision Processes (MDP) (15 books)

Book	Author	Pages / Concepts
10.marl	—	~22–23, ~26–28, ~184–194, ~242–265, ~312
2.Foundation of LLM	—	186–188, 194–199
3.Reinforcement Learning- An Overview	Kevin Murphy	13, 22–27, 31–35, 88–90, 98–99, 107–108
4.alg4ai	—	51–56
6 390 lecture notes spring24	—	88–91
Essential Math for AI	Hala Nelson	Hamilton-Jacobi-Bellman Equation; Markov Decision Processes and Reinforcem; Reinforcement Learning as a Markov Decis (+1)
Foundations of Machine Learning	Mohri, Rostamizadeh & Talwalkar	327
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	683–688
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	727–730
Machine Learning: An Algorithmic Perspective, Second Edition	—	~238–239
MachineLearningNotes	—	245–246
Neural Networks and Deep Learning: A Textbook	—	399
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	232, 395
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	17–19, 27–29, 48–53
UnderstandingDeepLearning 02 09 26 C [Reading]	—	388–391

Dynamic Programming (Value/Policy Iteration) (13 books)

Book	Author	Pages / Concepts
10.marl	—	~29–31, ~116–117
3.Reinforcement Learning- An Overview	Kevin Murphy	18, 33–34
4.alg4ai	—	64–68
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	671–676
Artificial Intelligence: With an Introduction to Machine Learning	—	437–440
Essential Math for AI	Hala Nelson	Hamilton-Jacobi-Bellman PDE for Dynamic ; Dynamic programming and reinforcement le
Introduction to Artificial Intelligence	—	306–308
Introduction to Machine Learning with Applications in Information Securit	—	39–40
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	518–521
MachineLearningNotes	—	250
Neural Networks and Deep Learning: A Textbook	—	131–132
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	491
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	15–25, 47, 56–57, 65–74, 79–83

Monte Carlo & Temporal Difference (5 books)

Book	Author	Pages / Concepts
3.Reinforcement Learning- An Overview	Kevin Murphy	36–37
Essential Math for AI	Hala Nelson	Monte Carlo Methods
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	689–690
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	731
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	396

Q-Learning & SARSA (11 books)

Book	Author	Pages / Concepts
3.Reinforcement Learning- An Overview	Kevin Murphy	38–40, 44, 107
6 390 lecture notes spring24	—	97–101
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	689–702
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	732–741
Introduction to Artificial Intelligence	—	311–314
Machine Learning: An Algorithmic Perspective, Second Edition	—	~245
MachineLearningNotes	—	253–256
Neural Networks and Deep Learning: A Textbook	—	403–404
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	594
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	391, 403–404, 407
UnderstandingDeepLearning 02 09 26 C [Reading]	—	400–402

Deep Q-Networks (DQN) (7 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	1301
3.Reinforcement Learning- An Overview	Kevin Murphy	41–42, 45–46
6 390 lecture notes spring24	—	100
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	693–702
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	735–743
Machine-Learning-Systems	—	1280–1281
MachineLearningNotes	—	255–256

Policy Gradient & Actor-Critic (9 books)

Book	Author	Pages / Concepts
10.marl	—	~195–214, ~230–241
3.Reinforcement Learning- An Overview	Kevin Murphy	49–57, 60–63, 66–68, 107
6 390 lecture notes spring24	—	101
9.finetuning guide	—	52–55
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	678–682
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	722–726
Neural Networks and Deep Learning: A Textbook	—	407, 411–413
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	134–142
UnderstandingDeepLearning 02 09 26 C [Reading]	—	403–408

Exploration vs Exploitation & Bandits (20 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	867–875
3.Reinforcement Learning- An Overview	Kevin Murphy	15, 21–24, 46
6 390 lecture notes spring24	—	102–103
Artificial Intelligence: With an Introduction to Machine Learning	—	348–350
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	307–310
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	691
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	734
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	30, 126, 156–169, 196–203, 208, 231–232, 261–262
Introduction to Artificial Intelligence	—	315
Machine Learning Algorithms with Applications in Finance	—	77–80
Machine Learning for Hackers	Conway & White	45
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	45
Machine Learning: An Algorithmic Perspective, Second Edition	—	~206
Machine-Learning-Systems	—	849–857
MachineLearningNotes	—	251–252
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	290
Neural Networks and Deep Learning: A Textbook	—	391
Principles And Theory For Data Mining And Machine Learning	—	656–659
Real-World Machine Learning	—	144–145
Statistical Reinforcement Learning: Modern Machine Learning Approaches	—	65–70, 134–142

Multi-Agent RL (6 books)

Book	Author	Pages / Concepts
10.marl	—	~2–8, ~12–14, ~17–18, ~43, ~89, ~102–108, ~115, ~159–160, ~219, ~222, ~230–241, ~305–315, ~319, ~337–340
11.context-engineering	—	42, 55
13.Machine-Learning-Systems	—	1847–1849
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	444–448
Essential Math for AI	Hala Nelson	Game Theory and Multiagents
Machine-Learning-Systems	—	1822–1823, 1856–1858

Bayesian Networks (18 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	35, 51–60, 84–88, 149–153, 158, 161
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	532–536, 541–557, 609–617
Artificial Intelligence: With an Introduction to Machine Learning	—	162–169, 175–176, 180–182, 196, 276
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~261–272
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	271–284
Essential Math for AI	Hala Nelson	Explicit Density-Tractable: Fully Visibl; Bayesian Networks; Bayesian Networks (+1)
Introduction to Artificial Intelligence	—	171–182, 228–230, 254
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	22, 138, 147
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	155, 308–310
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	82
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	154–155
Machine Learning in Healthcare Informatics	—	123, 129, 274–275
Machine Learning: An Algorithmic Perspective, Second Edition	—	~322–329, ~359
Machine Learning: Hands-On for Developers and Technical Professionals	—	95, 101, 105–106
MachineLearningNotes	—	85–89, 106
mml-book [Reading]	—	284–288
Neural Networks and Deep Learning: A Textbook	—	285
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	657–659, 664, 674

Markov Models & HMM (14 books)

Book	Author	Pages / Concepts
12.LAEF	—	Stochastic Matrices & Markov Chains
7.pen and paper exercise in ML	—	46, 67–70, 119–126, 131–134, 189–192
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	597–602
Essential Math for AI	Hala Nelson	Explicit Density-Intractable: Boltzman M; Implicit Density-Markov Chain: Generativ; Markov Chain
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	226–227, 240–244
Handbook of Statistics: Machine Learning: Theory and Applications	—	~421–442
Introduction to Machine Learning with Applications in Information Securit	—	26–27, 33–38, 56–57, 256–258, 270–279
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	153–155
Machine Learning with TensorFlow	—	~119–124, ~130
Machine Learning: An Algorithmic Perspective, Second Edition	—	~305–307, ~313–318, ~330–342
Principles And Theory For Data Mining And Machine Learning	—	367–368
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	351–354, 457–461, 478–480, 488–494, 500–503, 507–508, 606–612, 616–617, 627–628, 665
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	233–235, 239–240, 257, 274
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Markov chain; Hidden Markov model

Conditional Random Fields (5 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	378–400
Handbook of Statistics: Machine Learning: Theory and Applications	—	~227–248
Introduction to Machine Learning with Applications in Information Securit	—	227–228
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)	—	108, 115–122
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	257–262, 269, 274

Gaussian Processes (6 books)

Book	Author	Pages / Concepts
Advances in Financial Machine Learning	López de Prado	357–358
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	—	65–66
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	39–40, 192
Machine Learning: An Algorithmic Perspective, Second Edition	—	~395–411
Principles And Theory For Data Mining And Machine Learning	—	353–358
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	359–365

Variational Inference & EM (3 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	199–204
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~249–251
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	—	359

Latent Variable Models (9 books)

Book	Author	Pages / Concepts
7.pen and paper exercise in ML	—	162–163
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	Rogers & Girolami	~239–241, ~248
Financial Signal Processing and Machine Learning	—	158–168
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	196–198
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	167–168
Machine Learning: An Algorithmic Perspective, Second Edition	—	~141
mml-book [Reading]	—	345–348
Principles And Theory For Data Mining And Machine Learning	—	515–516
UnderstandingDeepLearning 02 09 26 C [Reading]	—	341–344

Model Deployment & Serving (29 books)

Book	Author	Pages / Concepts
11.context-engineering	—	56
13.Machine-Learning-Systems	—	82–85, 88, 114–119, 126–127, 138–141, 145–146, 221–223, 234, 351, 354–358, 361, 427–429, 461–462, 557, 566, 579–580, 655, 788, 1127–1128, 1138–1139, 1188–1193, 1235, 1318, 1328–1329, 1336–1337, 1410–1411, 1583–1584, 1746, 1756, 1794, 1839–1842, 1899–1900, 1959–2022, 2029–2046, 2083–2108, 2113–2131, 2135–2166, 2169–2194, 2197–2244, 2302–2313, 2326–2357
9.finetuning guide	—	14, 18, 66–73, 82
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	96–99, 168–170, 174–175, 180–191, 194–195
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	32–41, 211–214, 283, 301–302, 340
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	387
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	749–759, 769–771
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	35
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~359
Keras to Kubernetes: The Journey of a Machine Learning Model to Production	—	2, 235–253
Language Models Interview Handbook	—	23, 30, 51, 65–69, 72–73, 94, 112–120, 125
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~71
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	228, 244
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	15–16
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	293–294, 325
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	50
Machine Learning in Python	—	133–134, 157, 206–211, 225–237
Machine Learning with TensorFlow 1x	—	209–211, 216–217
Machine Learning: Hands-On for Developers and Technical Professionals	—	48
Machine-Learning-Systems	—	66–69, 72, 98–103, 109, 122–125, 129–130, 205–207, 218, 333, 336–340, 343, 409–411, 443–444, 540, 548–549, 562–563, 638–642, 769–770, 1108, 1119–1120, 1170–1175, 1216, 1298, 1308, 1316, 1390–1391, 1563–1564, 1744, 1754, 1792, 1844–1845, 1849–1851, 1895–1896
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	91
Practical Machine Learning with Python	—	~255, ~302–303
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	73–74, 82, 273, 320–322
Predictive Analytics	—	44
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	59–60, 103–106, 115–119, 206–209, 223–225, 253
Pro Machine Learning Algorithms	—	32
Python Machine Learning	—	280–295
Real-World Machine Learning	—	40, 213–214
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	462

ML Pipelines & Workflows (27 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	220–228, 337, 399–400, 422–423, 435–437, 461–462, 558, 638–645, 653, 656–660, 757, 1003, 1183–1186, 1463, 2005–2022, 2135–2166
9.finetuning guide	—	13, 16
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	25–27, 75, 94–95
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	344, 365–366, 387–388
Blockchain Enabled Applications: Understand the Blockchain Ecosystem and How to Make it Work for You	—	28–33, 138–141
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	121
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	21–22
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	90–91, 223–225, 334–338, 345–346
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	17
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	109–110
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	111–115
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~305, ~308–316
Language Models Interview Handbook	—	51, 54, 57, 118
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~190–198
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	95–96
Machine learning con Python: costruire algoritmi per generare conoscenza	—	251–252
Machine Learning Mastery with Python	Jason Brownlee	96–98
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	21
Machine Learning with TensorFlow 1x	—	157–158, 199–202, 230, 235–238
Machine Learning Yearning (Draft Version)	Andrew Ng	97–102, 115–117
Machine Learning: Hands-On for Developers and Technical Professionals	—	273–275
Machine-Learning-Systems	—	204–212, 319, 381–382, 404–405, 417–419, 443–444, 540, 620, 623–628, 636–642, 739, 985, 1164–1168, 1443
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python	—	84
Practical Machine Learning with Python	—	~119–120, ~179–180
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	73–76, 139, 198–199
Real-World Machine Learning	—	24–25, 40, 219, 226
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	51–53, 67–68, 73–76, 222, 444–445

Containers & Orchestration (4 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2026–2027
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	328–330
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	501–503
Keras to Kubernetes: The Journey of a Machine Learning Model to Production	—	2, 235–253

Monitoring, Versioning & CI/CD (12 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	134–135, 360–362, 401–402, 756, 1169–1171, 1194–1198, 1228, 1319–1322, 1412–1416, 1489–1490
9.finetuning guide	—	18, 61, 73–75
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	326, 330
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	182–187, 245, 270, 276–278, 313–316
Handbook of Statistics: Machine Learning: Theory and Applications	—	~353–380
Language Models Interview Handbook	—	94
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	275–276
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	328
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	305
Machine Learning: Hands-On for Developers and Technical Professionals	—	119
Machine-Learning-Systems	—	118–119, 342–344, 383–384, 738, 1151–1153, 1176–1180, 1209, 1299–1302, 1392–1396, 1469–1470
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	529–531

Scalability & Distributed Training (27 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	567–568, 678–696, 920, 1020–1023, 1939–1958
2.Foundation of LLM	—	67–69
9.finetuning guide	—	67–68
Advances in Financial Machine Learning	López de Prado	167
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	264–268, 271–272
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	60
Blockchain Enabled Applications: Understand the Blockchain Ecosystem and How to Make it Work for You	—	167–168
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	188–191
Essential Math for AI	Hala Nelson	Cayley Graphs of Groups: Pure Algebra an
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence	—	469–471
grokking-deep-learning	—	~297
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	388, 438, 469–471, 523
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	483–485, 490–491, 519, 525–528
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	775–792
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	181
Kernel-book-rev5 [Kernal boom]	—	107
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	181, 192–195
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	318–322
Machine learning con Python: costruire algoritmi per generare conoscenza	—	534–536
Machine learning in action	Peter Harrington	327–328
Machine Learning in Action	Peter Harrington	327–328
Machine Learning in Python	—	74–75, 90–93
Machine Learning with TensorFlow 1x	—	244–256, 287
Machine Learning Yearning (Draft Version)	Andrew Ng	32–33
Machine-Learning-Systems	—	550, 660–679, 902, 1001–1005
Neural Networks and Deep Learning: A Textbook	—	177–179
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	432–433

Spark / Hadoop / Distributed Data (11 books)

Book	Author	Pages / Concepts
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	261, 273–274, 279–289
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	11–38, 56–57, 95, 143–146, 154, 225, 236–238, 293–294, 326, 333–334, 344
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	36–40, 142–145, 278
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	186–193, 199
Machine learning in action	Peter Harrington	326–329, 332–342, 345–349
Machine Learning in Action	Peter Harrington	326–329, 332–342, 345–349
Machine Learning: Hands-On for Developers and Technical Professionals	—	39, 251–254, 259–272, 286–288, 301, 306–321, 331, 368–370, 377–380, 394
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI	—	42–43
Practical Machine Learning with H2O	—	20–24, 51, 70–74, 79–80, 323–324, 328
ProgrammerLazy. SQL for Marketers: Dominate data analytics, data science, and big data. Data Science and Machine Learning in Python	—	74–83
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	456–457, 462–466

Python ML Stack (NumPy/Pandas/scikit-learn) (23 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2275–2286
Advances in Financial Machine Learning	López de Prado	154
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	88–89
Basics of Linear Algebra for Machine Learning	Jason Brownlee	32–33, 39–40, 51–54, 166, 186
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	—	99–105, 130–132, 156–158
grokking-deep-learning	—	~44–45, ~181–184
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	286–287, 327
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	245, 274, 435–437
Introduction to Machine Learning with Python ( PDFDrive.com )-min	Müller & Guido	~5–6
Machine learning con Python: costruire algoritmi per generare conoscenza	—	90–91, 94–100, 109–112, 123–124, 160–162, 208–212, 221–224, 244–245, 355–358, 402–404, 461
Machine learning in action	Peter Harrington	42–43, 54–55, 75–77, 228–229, 300–301, 357–359
Machine Learning in Action	Peter Harrington	42–43, 54–55, 75–77, 228–229, 300–301, 357–359
Machine Learning in Python	—	71–73
Machine Learning Mastery with Python	Jason Brownlee	19, 28–33, 37, 178–179
Machine Learning with TensorFlow	—	~38–40
Machine Learning: An Algorithmic Perspective, Second Edition	—	~423–429
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	95–97, 107, 118–119, 322–323
Mastering Machine Learning with scikit-learn 2nd edition	—	27–30, 132–135, 149–150, 184–186
Practical Linear Algebra for Data Science - Mike X Cohen	—	Creating and Visualizing Vectors in NumP; Creating and Visualizing Matrices in Num; NumPy (+1)
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	40, 51
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	92, 95, 102–104, 116, 119–121, 171–172, 180
Pro Machine Learning Algorithms	—	349, 367–370
Python Machine Learning	—	37–133

Deep Learning Frameworks (TensorFlow/PyTorch/Keras) (22 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	40–43, 55–57
10.marl	—	~307–311
13.Machine-Learning-Systems	—	559–561, 2275–2286
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	—	~1–30, ~105–113, ~206–210
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	15–20
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python	—	41–69
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow	—	45–69
Deep Learning. Practical Neural Networks with Java	—	218–227
grokking-deep-learning	—	~294
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	346–349, 355–356, 399–401, 429–431, 435–436, 477–478, 504, 519, 532–533, 547–549, 582–583, 633–634, 647–648, 652–653, 724–727, 733–735
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	327, 345, 403–404, 431–436, 461–462, 469, 480, 484, 487, 503–504, 515–517, 521–522, 543–545, 669, 749–759, 794–797, 829–830, 837–838
Keras to Kubernetes: The Journey of a Machine Learning Model to Production	—	2, 126–144
Machine learning con Python: costruire algoritmi per generare conoscenza	—	534–543, 560–565
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	209
Machine Learning with TensorFlow	—	~21, ~25–27, ~100–101, ~182–186
Machine Learning with TensorFlow 1x	—	21–22, 64, 211, 263, 267–273, 287–291
Machine-Learning-Systems	—	541–543
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	329–331
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	17
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	125–128
Pro Machine Learning Algorithms	—	258–265
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	TensorFlow Model

Streaming & Real-Time (19 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	126–127, 420–421, 1299–1300
3.Reinforcement Learning- An Overview	Kevin Murphy	34
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	48–50
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	190–195
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	19, 225, 236–238, 248–254, 263–267, 272–274, 293–294, 306–308, 326–330
Building Chatbots with Python: Using Natural Language Processing and Machine Learning	—	153–154
Demystifying Big Data and Machine Learning for Healthcare	—	152–154
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	94–97, 223–225
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	29
Language Models Interview Handbook	—	115
LEARNI~1	—	81–82, 86–87, 90–91
LLM Interview	—	19–22
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~10–11
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	206
Machine learning in action	Peter Harrington	329
Machine Learning in Action	Peter Harrington	329
Machine Learning: Hands-On for Developers and Technical Professionals	—	213–214, 241–242, 331
Machine-Learning-Systems	—	110–111, 402–403, 1279
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python	—	143–144

Finance & Trading (38 books)

Book	Author	Pages / Concepts
1.Deep learning Interviews	Shlomo Kashani	147, 169
13.Machine-Learning-Systems	—	2300–2301
6 390 lecture notes spring24	—	113–118
8.matrixcookbook	Petersen & Pedersen	8–16, 24–26
Advances in Financial Machine Learning	López de Prado	26–29, 51–53, 143, 147–148, 225, 229–231, 297, 330, 362–364
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	26–30, 43–51
Artificial Intelligence: With an Introduction to Machine Learning	—	262, 341–345, 385–387
Blockchain Enabled Applications: Understand the Blockchain Ecosystem and How to Make it Work for You	—	192
Demystifying Big Data and Machine Learning for Healthcare	—	158–161
Essential Math for AI	Hala Nelson	Derivatives of linear algebra expression; 7. Natural Language and Finance AI: Vect; Finance AI (+1)
Financial Signal Processing and Machine Learning	—	21–26, 30, 34–41, 52–53, 184–192, 244–246, 286–291
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	Rasmussen & Williams	185–186, 191
grokking-deep-learning	—	~68–70, ~173
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	Aurélien Géron	676–677
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	Aurélien Géron	721
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	57
Language Models Interview Handbook	—	109
Machine Learning Algorithms with Applications in Finance	—	8, 83–84, 91, 98–135
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	177
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	21
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	266–268
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	265–285, 296–297, 300–303, 330–331, 352–353, 360–362, 368–376
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	92, 230–234
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	354
Machine Learning: Hands-On for Developers and Technical Professionals	—	31, 118–119
MachineLearningNotes	—	31
mml-book [Reading]	—	170
Neural Networks and Deep Learning: A Textbook	—	36, 47, 163–164
Numerical algorithms : methods for computer vision, machine learning, and graphics	—	277–278
Practical Machine Learning with Python	—	~467–473, ~483–496
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	483–484, 499
Predictive marketing : easy ways every marketer can use customer analytics and big data	—	87
Principles And Theory For Data Mining And Machine Learning	—	498
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	37–38
Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data	—	376–380, 407, 421–422, 492–497
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods	—	88–92, 106–107, 111–115, 118–121, 135–140, 155, 161, 166, 170–173, 178–180, 183–186, 192, 207, 257, 286
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Derivative of a function; Higher Order derivatives; Derivative of scalar fields w.r.t. vecto (+1)
UnderstandingDeepLearning 02 09 26 C [Reading]	—	111–113

Healthcare & Bioinformatics (23 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	134–135, 359, 1747
9.finetuning guide	—	93
Blockchain Enabled Applications: Understand the Blockchain Ecosystem and How to Make it Work for You	—	126–129, 136–137, 146–147, 206–214
Deep Learning. Practical Neural Networks with Java	—	619, 628–634
Demystifying Big Data and Machine Learning for Healthcare	—	30–35, 38–40, 48–53, 96, 99–104, 116–124, 158–159, 180, 196–198
Essential Math for AI	Hala Nelson	Spread of Disease; Molecular Graph Generation for Drug and
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	583, 594–595, 631–632, 636–637, 643–645
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	185–186, 204–208
introduction-to-algorithms-and-machine-learning	—	129–132
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~12–16, ~44–50, ~237, ~255, ~278, ~328–334
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	144–145
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	—	22–24
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	17, 24–28, 31–48, 51–65, 90, 133, 147–151, 154–156
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	99, 235–242
Machine learning in bioinformatics	—	1–68, 89–134, 189–240, 321–338, 389–412, 431–461
Machine Learning in Healthcare Informatics	—	13–15, 21–23, 27, 31, 140–142, 215–217, 265–271
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	—	~109–125
Machine Learning with TensorFlow 1x	—	177–183
Machine Learning: Hands-On for Developers and Technical Professionals	—	32, 119
Machine-Learning-Systems	—	118–119, 341, 1745
Neural Networks and Deep Learning: A Textbook	—	327
Probability, Random Processes, and Statistical Analysis: Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance	—	37–38
Signal Processing and Machine Learning for Brain–Machine Interfaces	—	336

Recommender Systems (28 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	2275–2286
Advances in Financial Machine Learning	López de Prado	205
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	—	258–259
Basics of Linear Algebra for Machine Learning	Jason Brownlee	31
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark Sql, Structured Streaming and Spark Machine Learning Library	—	380
Deep Learning. Practical Neural Networks with Java	—	343–346, 350–365
Essential Math for AI	Hala Nelson	Web-Scale Recommendation Systems
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	Zheng & Casari	175–191
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	129–131, 225
Introduction to Deep Learning Using R: A Step-by-Step Guide to Learning and Implementing Deep Learning Models Using R	—	227–230
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	171
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	226–241
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	91
Machine Learning for Hackers	Conway & White	249–254
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	249–254
Machine learning in action	Peter Harrington	309, 313, 316–324
Machine Learning in Action	Peter Harrington	309, 313, 316–324
Machine Learning Refined: Foundations, Algorithms, and Applications	Watt, Borhani & Katsaggelos	338
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems	—	123–152
Machine Learning: Hands-On for Developers and Technical Professionals	—	276–281
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python	—	269, 309–312
Neural Networks and Deep Learning: A Textbook	—	103–105, 259, 272–274, 325–326
Practical Machine Learning with Python	—	~55–63, ~447, ~456–466
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems	—	76, 463, 472–482
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Editio	—	39, 257–258
Predictive marketing : easy ways every marketer can use customer analytics and big data	—	57–59, 161, 167–168
Pro Machine Learning Algorithms	—	307, 310, 320–322
Real-World Machine Learning	—	133–134

Security & Anomaly/Fraud Detection (25 books)

Book	Author	Pages / Concepts
11.context-engineering	—	57
13.Machine-Learning-Systems	—	466–467, 1359–1364, 1383, 1417–1430
9.finetuning guide	—	101
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing	—	64–67
Deep Learning. Practical Neural Networks with Java	—	367, 371–378, 426–428, 444–448
Essential Math for AI	Hala Nelson	Spam Filter
grokking-deep-learning	—	~284–285
Handbook of Natural Language Processing, Second Edition (Chapman & Hall CRC Machine Learning & Pattern Recognition Series	—	682–685
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	—	35
Introduction to Machine Learning with Applications in Information Securit	—	259–260, 288, 299–300, 303–308, 315–318, 326, 333–335
Kernel-book-rev5 [Kernal boom]	—	28
LEARNI~1	—	135–136, 139–140
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~223
Machine Learning and Cognition in Enterprises: Business Intelligence Transform	—	94, 301
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	19–20, 30–42, 99, 103–110, 143–162, 196, 202–210, 266–268, 279, 295–296, 329, 336–337, 369
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	352–353
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making	—	164, 281–283
Machine Learning for Hackers	Conway & White	89–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	Conway & White	89–100
Machine learning in action	Peter Harrington	101
Machine Learning in Action	Peter Harrington	101
Machine Learning in Healthcare Informatics	—	265–266, 270–271, 285–286
Machine-Learning-Systems	—	448–449, 1339–1344, 1363, 1397–1410
Mastering Machine Learning with scikit-learn 2nd edition	—	106
Predictive Analytics	—	67

Explainability & Interpretability (9 books)

Book	Author	Pages / Concepts
13.Machine-Learning-Systems	—	1571, 1583–1584
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~365–368
Explainable and Interpretable Models in Computer Vision and Machine Learning	—	~3–18, ~81–134, ~173–196, ~255–277
Language Models Interview Handbook	—	125
Machine Learning and Security: Protecting Systems with Data and Algorithms	—	139–140, 311–314
Machine Learning with TensorFlow	—	~121–123
Machine-Learning-Systems	—	1551, 1563–1564
Neural Networks and Deep Learning: A Textbook	—	90
Tamoghna Ghosh, Shravan Kumar Belagal Math - Practical Mathematics for AI and Deep Learning	—	Interpretability of linear models

Fairness, Bias & Ethics (16 books)

Book	Author	Pages / Concepts
10.marl	—	~78–80
11.context-engineering	—	57
13.Machine-Learning-Systems	—	47–50, 91, 415–417, 447–448, 471, 759, 1563–1567, 1571–1576, 1581–1585, 1623, 1666–1668, 1731–1732
9.finetuning guide	—	23, 100–102
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	Andrew Hinton	6. AI Ethics and Responsible Management:; 10. Ethical Considerations and Responsib
Artificial Intelligence - A Modern Approach (3rd Edition)	Russell & Norvig	1053–1058
Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies	—	100–111
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	Witten, Frank & Hall	~33–35
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	Witten, Frank & Hall	35–36
Data Science	—	197–234
Designing Machine Learning Systems - Chip Huyen [Reading]	Chip Huyen	359–372
Essential Math for AI	Hala Nelson	14. Artificial Intelligence, Ethics, Mat; Addressing Fairness; Distinguishing Bias from Discrimination
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes	—	~66–70, ~207–211, ~224–233, ~241–243, ~249–252, ~328–334
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance	—	181, 288–295
Machine-Learning-Systems	—	36–38, 76–77, 397–399, 429–430, 453, 740, 1543–1547, 1551–1556, 1561–1565, 1603, 1647, 1714–1715
UnderstandingDeepLearning 02 09 26 C [Reading]	—	26–28, 435–440, 443–444, 447