π₯ Deep Learning with PyTorch
From raw tensors and autograd to a trained, exported, served model β the canonical PyTorch 2.x path, built by hand.
9-Lesson mini-course β From raw tensors and autograd to a trained, exported, served model β the canonical PyTorch 2.x path, built by hand.
Like every course on this site, each lesson is a deep, code-first lesson: an intuition-first explainer, a staged code walkthrough explaining the methodology line by line, visuals, and a π§ͺ Your task exercise with a hidden solution. Theory lives in the AI & ML Encyclopedia; here you build.
βΆ Start Lesson 1 π All mini-courses
Syllabus
| # | Lesson |
|---|---|
| Lesson 1 | Tensors & Autograd: The Foundation |
| Lesson 2 | Building Models with nn.Module |
| Lesson 3 | Feeding the Model: Dataset & DataLoader |
| Lesson 4 | The Training Loop, Properly |
| Lesson 5 | Classification End to End: An MLP on Real Data |
| Lesson 6 | Convolutional Networks: Teaching Models to See |
| Lesson 7 | Training Deeper Networks: Stability & Regularization |
| Lesson 8 | Transfer Learning: Stand on ImageNetβs Shoulders |
| Lesson 9 | Saving, Exporting & Serving Your Model |