Library

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	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	6, 10, 26, 43
6 390 lecture notes spring24	71–72, 114–116
7.pen and paper exercise in ML	24–26
8.matrixcookbook	10–11, 14–16, 61
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~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]	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)	763–766, 1074–1075
Basics of Linear Algebra for Machine Learning	60–66, 69–71, 82, 196
Essential Math for AI	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	22–23, 58–60, 120, 199–200
Financial Signal Processing and Machine Learning	346–362
Foundations of Machine Learning	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)	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	225, 715–716
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	291–299
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	291–299
Machine learning in action	60–61, 94–97, 128, 345–348
Machine Learning in Action	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	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	Pages / Concepts
10.marl	~321–322
12.LAEF	Special Matrices; Matrix Representations; Adjoints & Transposes (+1)
13.Machine-Learning-Systems	932–934
5.math4ml	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	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	~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)	1074–1075
Basics of Linear Algebra for Machine Learning	25, 59, 74–84, 87–93, 96, 106–110, 125, 133, 136, 141–142, 159, 170, 197–199
Essential Math for AI	The One-Dimensional Case: Multiplication; The Two-Dimensional Case: Multiplication; Matrix Factorization (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	120, 198, 203–204
Financial Signal Processing and Machine Learning	157, 169–183
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	138–140
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	310, 362–367
Machine Learning in Action	310, 362–367
Machine learning in bioinformatics	69–88
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
12.LAEF	Inverse & Invertibility; The Pseudoinverse
5.math4ml	16, 25
7.pen and paper exercise in ML	17–18, 32–33
8.matrixcookbook	6–8, 21–23
Basics of Linear Algebra for Machine Learning	100, 143–144, 176–177
Essential Math for AI	The Pseudoinverse
introduction-to-algorithms-and-machine-learning	167–206
Machine learning in action	365
Machine Learning in Action	365
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
12.LAEF	Eigenvalues & Eigenvectors; Eigenvalue Complexities; Complex Eigenvalues & Oscillation (+1)
5.math4ml	15
7.pen and paper exercise in ML	15–18
8.matrixcookbook	10–11, 30
Basics of Linear Algebra for Machine Learning	132–135
Essential Math for AI	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)	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	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	Pages / Concepts
12.LAEF	Singular Value Decomposition; Spheres, Ellipsoids, & Singular Values; Constructing the SVD (+1)
5.math4ml	20
8.matrixcookbook	31
Basics of Linear Algebra for Machine Learning	139, 176–177
Essential Math for AI	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	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	307–308, 311–312, 319–324
Machine Learning in Action	307–308, 311–312, 319–324
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
12.LAEF	LU Decomposition; The QR Decomposition
8.matrixcookbook	32
Basics of Linear Algebra for Machine Learning	126–129, 174–175
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	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	25, 125
Essential Math for AI	Matrix Factorization
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	103–104, 314, 329
Machine learning in action	310
Machine Learning in Action	310
Machine learning in bioinformatics	69–88
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
12.LAEF	Inner Products & Orthogonality; Angles & Orthogonality; Orthogonal & Orthonormal Bases (+1)
3.Reinforcement Learning- An Overview	43
5.math4ml	12–16, 22–23
7.pen and paper exercise in ML	10–12
8.matrixcookbook	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	69–71, 92–93
Essential Math for AI	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	466–468
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	366
Machine Learning in Action	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	Pages / Concepts
12.LAEF	Subspaces; Span & Linear Independence; Orthogonal Subspaces & Complements (+1)
5.math4ml	7, 12–14
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~322–329
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	290, 317–319
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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	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]	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	Pages / Concepts
1.Deep learning Interviews	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	8–16, 24–26
Essential Math for AI	Derivatives of linear algebra expression
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	765–769
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
1.Deep learning Interviews	144, 147, 166, 169
5.math4ml	28, 38
6 390 lecture notes spring24	116
Essential Math for AI	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	Pages / Concepts
10.marl	~169–174, ~195–214, ~230–241
13.Machine-Learning-Systems	212–215, 667–673
3.Reinforcement Learning- An Overview	49, 54–55, 66, 107
5.math4ml	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	155–158
grokking-deep-learning	~234–235
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	300–305, 432, 448–449
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	113–122
Machine Learning in Action	113–122
Machine Learning in Python	270–283, 297–300, 303, 312–317, 325–335, 341–347
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
1.Deep learning Interviews	145–146, 167
5.math4ml	29–30
Machine Learning Refined: Foundations, Algorithms, and Applications	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	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	62
9.finetuning guide	103
Essential Math for AI	Random Variable, Expectation, and Integr
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	340
Machine Learning in Action	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	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	6. Numerical Methods: Solving Equations
Essential Math for AI	Finite Differences
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	814
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
1.Deep learning Interviews	141, 149–150, 163, 172
12.LAEF	Stochastic Gradient Descent
3.Reinforcement Learning- An Overview	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]	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	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	176–189, 357
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
12.LAEF	Stochastic Gradient Descent
3.Reinforcement Learning- An Overview	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	Stochastic Gradient Descent
grokking-deep-learning	~109
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	183–189
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	173–176
Machine learning in action	118–122
Machine Learning in Action	118–122
Machine Learning Refined: Foundations, Algorithms, and Applications	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	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	446–449
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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	450–452
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	453–454
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	412–415
Neural Networks and Deep Learning: A Textbook	160
UnderstandingDeepLearning 02 09 26 C [Reading]	102–104

Learning Rate & Schedules (13 books)

Book	Pages / Concepts
1.Deep learning Interviews	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	Explaining the Role of the Learning Rate
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	455–457
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
Artificial Intelligence - A Modern Approach (3rd Edition)	166–171
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
5.math4ml	31–35
6 390 lecture notes spring24	39
Advances in Financial Machine Learning	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	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	362–371
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	206
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	247–250, 760–763
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	41
Essential Math for AI	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	Pages / Concepts
Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimizatio	56–58
Essential Math for AI	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	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	Pages / Concepts
1.Deep learning Interviews	61, 79–82
5.math4ml	37–41
7.pen and paper exercise in ML	149–153, 158, 176–178
8.matrixcookbook	34
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~41–52, ~55–57
Advances in Financial Machine Learning	191–192, 292–294
AI Mastery Trilogy- A Comprehensive Guide to AI by 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]	2.2 Random Variable
Artificial Intelligence - A Modern Approach (3rd Edition)	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	~159–162, ~343–345
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	157–160
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	160–162
Designing Machine Learning Systems - Chip Huyen [Reading]	103
Essential Math for AI	The Vocabulary of Data Distributions, Pr; Random Variables; Probability Distributions (+1)
Foundations of Machine Learning	372–381
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	210–211, 265
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	93
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	93
Machine learning in action	88–93, 96–97, 368–371
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	71, 93, 161–162, 191–199
13.Machine-Learning-Systems	1034, 1331–1333, 1489, 1506–1510
3.Reinforcement Learning- An Overview	64–65, 97
5.math4ml	39–41, 45–46
7.pen and paper exercise in ML	56, 120–121, 146–148, 156–157, 182–184, 193–194
8.matrixcookbook	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	~53–54
Advances in Financial Machine Learning	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)	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]	245, 257–269
Essential Math for AI	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)	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	237–238
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
1.Deep learning Interviews	58, 61–67, 71–75, 79–87, 93–98, 369–372
3.Reinforcement Learning- An Overview	17, 22–25
5.math4ml	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	~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]	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)	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	~261–272
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	271–284
Deep Learning. Practical Neural Networks with Java	314–317
Essential Math for AI	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)	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]	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	93–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	93–100
Machine learning in action	88–93, 101–104
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	68–70, 88–89
5.math4ml	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	~39–40, ~67–74, ~117–118, ~131–132
Advances in Financial Machine Learning	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	Prior Distribution, Posterior Distributi; Maximum Likelihood Estimation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	Pages / Concepts
1.Deep learning Interviews	59–60, 76–78
12.LAEF	Covariance & Correlation
5.math4ml	41–43
7.pen and paper exercise in ML	148
8.matrixcookbook	34–35, 42–43
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~75
Advances in Financial Machine Learning	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	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	Expectation, Mean, Variance, and Uncerta; Covariance and Correlation; Multivariate statistics: Wishart matrice (+1)
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	47, 119
Financial Signal Processing and Machine Learning	157–168
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	52–59, 74, 79–95
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	323, 328
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	197–198, 330–331
Machine Learning in Action	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)	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	Pages / Concepts
1.Deep learning Interviews	251–257, 308–312
12.LAEF	Covariance & Correlation
5.math4ml	43
Advances in Financial Machine Learning	319
Basics of Linear Algebra for Machine Learning	157–158
Essential Math for AI	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	95–98
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	168–170
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	168–170
Machine Learning in Python	83, 94–95
Machine Learning Mastery with Python	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	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	Pages / Concepts
13.Machine-Learning-Systems	2023–2025
3.Reinforcement Learning- An Overview	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	~154–164
Advances in Financial Machine Learning	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	~330
Designing Machine Learning Systems - Chip Huyen [Reading]	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	175–177
Machine Learning in Action	175–177
Machine learning in bioinformatics	89–110
Machine Learning in Python	260–262
Machine Learning Mastery with Python	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	Pages / Concepts
1.Deep learning Interviews	378–400
3.Reinforcement Learning- An Overview	35, 59, 72
7.pen and paper exercise in ML	175–179, 189–192, 195–198
Advances in Financial Machine Learning	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]	107
Essential Math for AI	Monte Carlo Methods
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	39, 55–57, 102–134, 364–365
3.Reinforcement Learning- An Overview	28
9.finetuning guide	58
Advances in Financial Machine Learning	348, 357–364
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	9. Information Theory: Quantifying and E
Artificial Intelligence: With an Introduction to Machine Learning	117–122
Essential Math for AI	Entropy and Gini impurity; Entropy and information gain
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	269
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	67–69
Machine Learning in Action	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	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	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~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	312–314
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	112–114
Designing Machine Learning Systems - Chip Huyen [Reading]	143–145
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	190
grokking-deep-learning	~145
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	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	124
Machine Learning in Action	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	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]	146–147, 326–327
Essential Math for AI	Normalizing, Scaling, and/or Standardizi; Batch Normalization of Each Layer
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	108, 426–431
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	~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	232–237
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	232–237
Machine learning in action	56–57
Machine Learning in Action	56–57
Machine Learning in Healthcare Informatics	18
Machine Learning Mastery with Python	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	Pages / Concepts
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	79–81
Basics of Linear Algebra for Machine Learning	29
Designing Machine Learning Systems - Chip Huyen [Reading]	149–151
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	93–94, 99
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	~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	148
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	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	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]	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	Pages / Concepts
13.Machine-Learning-Systems	430–431, 434
Advances in Financial Machine Learning	194
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~305–306, ~314–321
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	296–304
Designing Machine Learning Systems - Chip Huyen [Reading]	148
Essential Math for AI	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	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]	491–492
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~220–223
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	101
Machine Learning Mastery with Python	56
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
9.finetuning guide	19–20, 23
Advances in Financial Machine Learning	110
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	8.1 Handling Imbalanced Datasets
Designing Machine Learning Systems - Chip Huyen [Reading]	122–132
Introduction to Machine Learning with Applications in Information Securit	247–248
Language Models Interview Handbook	49
Machine learning in action	169, 175–177
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	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	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	~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	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	Pages / Concepts
Advances in Financial Machine Learning	158–167
Designing Machine Learning Systems - Chip Huyen [Reading]	162–163
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	54
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	293–294
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	~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	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	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	~239–247, ~252–259
Basics of Linear Algebra for Machine Learning	30, 163–166
Essential Math for AI	Principal Component Analysis and Dimensi; Principal Component Analysis and Cluster
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	115–116, 120–126
Foundations of Machine Learning	295–297
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	322–325, 331–335, 630–631
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	221–230
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	221–230
Machine learning in action	296–304
Machine Learning in Action	296–304
Machine Learning in Healthcare Informatics	43, 79
Machine Learning Mastery with Python	63
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
Foundations of Machine Learning	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	320–321, 335, 339–341
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	269–270, 282–283
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~140–167
Machine learning con Python: costruire algoritmi per generare conoscenza	225, 233, 244–245
Machine Learning for Hackers	231, 243–248
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	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	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	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	Pages / Concepts
7.pen and paper exercise in ML	164–165
Essential Math for AI	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	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	~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]	3.1 Linear Regression
Artificial Intelligence: With an Introduction to Machine Learning	106–108, 111–112
Basics of Linear Algebra for Machine Learning	25, 29, 169–172
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	173–178
Essential Math for AI	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	170–171, 355–356
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	149–156
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	149–156
Machine learning in action	181–189
Machine Learning in Action	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	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	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	190–192
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	177–178
Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R	149
Machine Learning for Hackers	174–180
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	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	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	When do we use plain linear regression,
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	200–206
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	206–208
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	206–208
Machine learning in action	191–196
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	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]	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	Logistic Regression: Classify into Two C
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	209
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	194–198
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	194–198
Machine learning in action	110–112, 125–126
Machine Learning in Action	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	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	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	Pages / Concepts
Essential Math for AI	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	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]	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	249–254
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	249–254
Machine learning in action	45, 50–51, 62
Machine Learning in Action	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	Pages / Concepts
Deep Learning. Practical Neural Networks with Java	314–317
Essential Math for AI	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	88, 92–93, 101–104
Machine Learning in Action	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	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	5. Supervised Learning Algorithms: Regre
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	3.3 Decision Tree Learning
Artificial Intelligence - A Modern Approach (3rd Edition)	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	~99–107, ~192–202, ~566–570
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	62–64, 97–104, 189–199
Essential Math for AI	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	259–261, 266–267, 717–718
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	64–65, 84–85, 211–212
Machine Learning in Action	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	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]	3.4 Support Vector Machine
Artificial Intelligence - A Modern Approach (3rd Edition)	763–766
Essential Math for AI	Support Vector Machines
Financial Signal Processing and Machine Learning	346–362
Foundations of Machine Learning	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)	141–145
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	225–232, 240–241, 251–254, 715–716, 760–763
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	291–299
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	291–299
Machine learning in action	128, 345–348
Machine Learning in Action	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	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	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	The kernel trick
Foundations of Machine Learning	102–104
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	233–234, 237–238
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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	390–398
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
Essential Math for AI	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	Pages / Concepts
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	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	Multi-class output
Explainable and Interpretable Models in Computer Vision and Machine Learning	~81–114
Foundations of Machine Learning	196–197, 204–215
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	48–51
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	152–154, 161–162
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
Machine learning in action	160–162
Machine Learning in Action	160–162
Machine Learning in Python	345–347

Bagging & Bootstrap Aggregation (20 books)

Book	Pages / Concepts
1.Deep learning Interviews	203–206, 215
6 390 lecture notes spring24	86
Advances in Financial Machine Learning	100–106, 135–139, 143–144
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~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	284–287
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	243–245
Machine learning con Python: costruire algoritmi per generare conoscenza	310–315
Machine learning in action	157
Machine Learning in Action	157
Machine Learning in Python	260–270, 284, 304–308
Machine Learning Mastery with Python	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	Pages / Concepts
6 390 lecture notes spring24	86
Advances in Financial Machine Learning	140
Essential Math for AI	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	279, 291, 719–720
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	203–206, 215
Advances in Financial Machine Learning	141–143
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~358–361
Foundations of Machine Learning	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	295, 300–305
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	158–159
Machine Learning in Action	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	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	Pages / Concepts
Foundations of Machine Learning	135–142
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	296–299
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	250–253
Introduction to Machine Learning with Applications in Information Securit	202–204
Machine learning in action	156, 163–168
Machine Learning in Action	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	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	300–305
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
1.Deep learning Interviews	203–206, 215
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~369–370
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	131–132
grokking-deep-learning	~118
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	280–283, 306–309, 544–546
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	~208–214
Data Science Essentials in Python: Collect - Organize - Explore - Predict - Value	179–181
Essential Math for AI	k-means Clustering
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	131–134, 138–142
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	234–243
Machine Learning in Action	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	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	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	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~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)	835–843
Essential Math for AI	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]	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	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by Andrew Hinton	6. Unsupervised Learning Algorithms: Clu
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~116–123, ~216–222, ~582–586
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	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	251–255, 258–263, 270–271, 275, 283
Machine Learning in Action	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	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	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	Pages / Concepts
1.Deep learning Interviews	248–250, 306–307
6 390 lecture notes spring24	23, 135
Advances in Financial Machine Learning	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	~152–153
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	149–150
Foundations of Machine Learning	18–19
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	111
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	114–116, 136–137
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	~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	102–103
Machine Learning in Action	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	68
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
A First Course in Machine Learning; Volume in Machine Learning and Pattern Recognition Series – CRC-Taylor & Francis-Chapman & Hall	~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	197–198
Machine Learning in Action	197–198
Machine Learning Made Easy with R: An Intuitive Step by Step Blueprint for Beginners	~316–318
Machine Learning Yearning (Draft Version)	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	Pages / Concepts
9.finetuning guide	60
Advances in Financial Machine Learning	220–221
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	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	54–57, 458
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	58–61, 420
Introduction to Artificial Intelligence	226–227
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~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	181–189
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	181–189
Machine Learning in Python	137–141, 144–152, 255
Machine Learning Refined: Foundations, Algorithms, and Applications	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	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]	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	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	Regularization Techniques; Commonly used weight decay regularizatio; Explaining the Role of the Regularizatio
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	84–87
Financial Signal Processing and Machine Learning	37–39
Foundations of Machine Learning	283–289
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	199, 270–271, 458–461, 466–468
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	171–173, 185–189
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	171–173, 185–189
Machine Learning in Healthcare Informatics	169
Machine Learning Refined: Foundations, Algorithms, and Applications	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	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	Dropout
grokking-deep-learning	~153–157
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	462–465, 607–608
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	422–426
Neural Networks and Deep Learning: A Textbook	207–209

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

Book	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	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	138–147
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	170–173
Machine Learning in Action	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	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	148–151
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	170–173
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	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	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	178–179, 183–185
Andriy Burkov - The Hundred-Page Machine Learning Book (2019, Andriy Burkov) [Reading]	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	~574–577
Essential Math for AI	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	84–87
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	118–120, 270–271, 336–338, 409
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	~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	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	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)	159–160, 168
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	193–198
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	179–182
Machine Learning Yearning (Draft Version)	60–65
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python	71

Multilayer Perceptron / Feedforward (37 books)

Book	Pages / Concepts
1.Deep learning Interviews	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)	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	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	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	383, 399–400, 728–730
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
1.Deep learning Interviews	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	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	395–398
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
1.Deep learning Interviews	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	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	Pass the result through a nonlinear acti; Common Activation Functions
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	166
grokking-deep-learning	~161–167
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	413, 422–425
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	111–112
Machine Learning in Action	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	Pages / Concepts
3.Reinforcement Learning- An Overview	64–65
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks	~90
Essential Math for AI	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	217–221
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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	420–421
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	387–388
UnderstandingDeepLearning 02 09 26 C [Reading]	121–124

Loss / Cost Functions (20 books)

Book	Pages / Concepts
13.Machine-Learning-Systems	209–211
6 390 lecture notes spring24	39, 55
9.finetuning guide	30
Advances in Financial Machine Learning	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]	60–62
Essential Math for AI	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	212–213
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	193–194, 440
Machine Learning and Security: Protecting Systems with Data and Algorithms	53, 363–368
Machine learning in action	174
Machine Learning in Action	174
Machine Learning Refined: Foundations, Algorithms, and Applications	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	Pages / Concepts
6 390 lecture notes spring24	63, 121–123
Essential Math for AI	Batch Normalization of Each Layer
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	426–431
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	418–419, 432
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
9.finetuning guide	31
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	188–189
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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	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)	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	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	162–165
grokking-deep-learning	~177–180
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	536, 539–546, 736–739
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pooling
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	169–172
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	552–553
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	Pages / Concepts
Neural Networks and Deep Learning: A Textbook	339–340

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

Book	Pages / Concepts
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	169–172
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	556–571
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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	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	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	536, 554–555, 736–739
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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]	8.7 Transfer Learning
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	117, 409, 433–434
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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	9. Computer Vision and Image Recognition
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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]	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	Pages / Concepts
1.Deep learning Interviews	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	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	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	Pages / Concepts
1.Deep learning Interviews	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	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	575, 602, 605–606, 740–741
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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	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	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	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	Gated Recurrent Units and Long Short-Ter
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	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	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	603–606
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	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]	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	621–624
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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]	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	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	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	107
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
big llm book	~12–18
Designing Machine Learning Systems - Chip Huyen [Reading]	153–154
Language Models Interview Handbook	35

Time Series Forecasting (19 books)

Book	Pages / Concepts
Advances in Financial Machine Learning	427
Artificial Intelligence: With an Introduction to Machine Learning	341–345
Deep Learning. Practical Neural Networks with Java	584–585
Essential Math for AI	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	596–601
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	23, 178–179, 199
Machine Learning in Action	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	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	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]	499–500
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools	248
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~334–335, ~339–346
Language Models Interview Handbook	16–17, 21
LLM Interview	4–5, 19–22
Machine learning in action	101
Machine Learning in Action	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	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	Term Frequency Vector Representation of ; Term Frequency-Inverse Document Frequenc
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	~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	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	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	619–620
Machine learning in action	94–97
Machine Learning in Action	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	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)	879–883
Essential Math for AI	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]	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	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	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	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by 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)	884–885
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~578–581
Deep Learning. Practical Neural Networks with Java	426–428, 444–448
Essential Math for AI	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]	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	~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	89–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	89–100
Machine learning in action	92–93
Machine Learning in Action	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	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	Topic Vector Representation of a Documen
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~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	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)	926–930
Essential Math for AI	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	621–624
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
AI Mastery Trilogy- A Comprehensive Guide to AI by 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	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	Pages / Concepts
1.Deep learning Interviews	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	Explicit Density-Intractable: Variationa; The Original Autoencoder
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	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]	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	Pages / Concepts
1.Deep learning Interviews	378–400
Essential Math for AI	Explicit Density-Intractable: Variationa
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	654–657
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
1.Deep learning Interviews	378–400
Essential Math for AI	Implicit Density-Direct: Generative Adve; How Do Generative Adversarial Networks W
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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]	663–664, 701–708
UnderstandingDeepLearning 02 09 26 C [Reading]	363

Boltzmann Machines & RBMs (9 books)

Book	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	Boltzmann Machine; Restricted Boltzmann Machine (Explicit D
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	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	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	Pages / Concepts
1.Deep learning Interviews	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	117, 409, 433–434, 442–444, 644–645, 757
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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]	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	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]	153–154
Essential Math for AI	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	619–620
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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	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)	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]	331–333
Essential Math for AI	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	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	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	~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)	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]	229–231
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	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	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	Hamilton-Jacobi-Bellman Equation; Markov Decision Processes and Reinforcem; Reinforcement Learning as a Markov Decis (+1)
Foundations of Machine Learning	327
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	683–688
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
10.marl	~29–31, ~116–117
3.Reinforcement Learning- An Overview	18, 33–34
4.alg4ai	64–68
Artificial Intelligence - A Modern Approach (3rd Edition)	671–676
Artificial Intelligence: With an Introduction to Machine Learning	437–440
Essential Math for AI	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	Pages / Concepts
3.Reinforcement Learning- An Overview	36–37
Essential Math for AI	Monte Carlo Methods
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	689–690
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
3.Reinforcement Learning- An Overview	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	689–702
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
13.Machine-Learning-Systems	1301
3.Reinforcement Learning- An Overview	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	693–702
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	735–743
Machine-Learning-Systems	1280–1281
MachineLearningNotes	255–256

Policy Gradient & Actor-Critic (9 books)

Book	Pages / Concepts
10.marl	~195–214, ~230–241
3.Reinforcement Learning- An Overview	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	678–682
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	Pages / Concepts
13.Machine-Learning-Systems	867–875
3.Reinforcement Learning- An Overview	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]	307–310
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	691
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	45
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	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	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)	444–448
Essential Math for AI	Game Theory and Multiagents
Machine-Learning-Systems	1822–1823, 1856–1858

Bayesian Networks (18 books)

Book	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)	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	~261–272
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	271–284
Essential Math for AI	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	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)	597–602
Essential Math for AI	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	Pages / Concepts
1.Deep learning Interviews	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	Pages / Concepts
Advances in Financial Machine Learning	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)	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	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	~249–251
Introduction to Pattern Recognition and Machine Learning [Murty & Devi 2014-09-30]	359

Latent Variable Models (9 books)

Book	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	~239–241, ~248
Financial Signal Processing and Machine Learning	158–168
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)	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	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]	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]	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	~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	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]	90–91, 223–225, 334–338, 345–346
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	17
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	109–110
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	111–115
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~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	96–98
Machine Learning Refined: Foundations, Algorithms, and Applications	21
Machine Learning with TensorFlow 1x	157–158, 199–202, 230, 235–238
Machine Learning Yearning (Draft Version)	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	Pages / Concepts
13.Machine-Learning-Systems	2026–2027
Designing Machine Learning Systems - Chip Huyen [Reading]	328–330
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems	501–503
Keras to Kubernetes: The Journey of a Machine Learning Model to Production	2, 235–253

Monitoring, Versioning & CI/CD (12 books)

Book	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]	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	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	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]	188–191
Essential Math for AI	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	483–485, 490–491, 519, 525–528
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	327–328
Machine Learning in Action	327–328
Machine Learning in Python	74–75, 90–93
Machine Learning with TensorFlow 1x	244–256, 287
Machine Learning Yearning (Draft Version)	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	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	326–329, 332–342, 345–349
Machine Learning in Action	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	Pages / Concepts
13.Machine-Learning-Systems	2275–2286
Advances in Financial Machine Learning	154
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	88–89
Basics of Linear Algebra for Machine Learning	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	286–287, 327
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	245, 274, 435–437
Introduction to Machine Learning with Python ( PDFDrive.com )-min	~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	42–43, 54–55, 75–77, 228–229, 300–301, 357–359
Machine Learning in Action	42–43, 54–55, 75–77, 228–229, 300–301, 357–359
Machine Learning in Python	71–73
Machine Learning Mastery with Python	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	Pages / Concepts
1.Deep learning Interviews	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	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]	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	Pages / Concepts
13.Machine-Learning-Systems	126–127, 420–421, 1299–1300
3.Reinforcement Learning- An Overview	34
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	48–50
Artificial Intelligence - A Modern Approach (3rd Edition)	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]	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	329
Machine Learning in Action	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	Pages / Concepts
1.Deep learning Interviews	147, 169
13.Machine-Learning-Systems	2300–2301
6 390 lecture notes spring24	113–118
8.matrixcookbook	8–16, 24–26
Advances in Financial Machine Learning	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	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)	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	676–677
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd ed [Reading]	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	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	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	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	Pages / Concepts
13.Machine-Learning-Systems	2275–2286
Advances in Financial Machine Learning	205
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning	258–259
Basics of Linear Algebra for Machine Learning	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	Web-Scale Recommendation Systems
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists	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	249–254
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	249–254
Machine learning in action	309, 313, 316–324
Machine Learning in Action	309, 313, 316–324
Machine Learning Refined: Foundations, Algorithms, and Applications	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	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	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	89–100
Machine Learning for Hackers: Case Studies and Algorithms to Get You Started	89–100
Machine learning in action	101
Machine Learning in Action	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	Pages / Concepts
13.Machine-Learning-Systems	1571, 1583–1584
Data Mining - Practical Machine Learning Tools and Techniques. Third edition	~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	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	6. AI Ethics and Responsible Management:; 10. Ethical Considerations and Responsib
Artificial Intelligence - A Modern Approach (3rd Edition)	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	~33–35
Data Mining Practical Machine Learning Tools and Techniques 2d ed - Morgan Kaufmann	35–36
Data Science	197–234
Designing Machine Learning Systems - Chip Huyen [Reading]	359–372
Essential Math for AI	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