Part 1: Deep Learning Fundamentals
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Brench
2026-05-08
2026-06-23
| Title | Author | Date |
|---|---|---|
| Chapter 1.1 Neural Networks: A Learnable Function | Brench | 2026-05-08 |
| Chapter 2.1 Automatic Differentiation in PyTorch | Brench | 2026-05-10 |
| Chapter 2.2 PyTorch Dimension Transformation Operations | Brench | 2026-05-11 |
| Chapter 2.3 Gradient Recording and Control in PyTorch | Brench | 2026-05-16 |
| Chapter 2.4 Data Loading in PyTorch: Dataset, DataLoader, and Batching | Brench | 2026-06-17 |
| Chapter 2.5 nn.Module in PyTorch: Organizing Models, Parameters, and State | Brench | 2026-06-18 |
| Chapter 2.6 Optimizer in PyTorch: From Manual Updates to Parameter Groups and State Management | Brench | 2026-06-18 |
| Chapter 2.7 Training Loop in PyTorch: Connecting Data, Model, and Optimizer | Brench | 2026-06-19 |
| Chapter 2.8 Checkpoints in PyTorch: Resuming Training After Interruption | Brench | 2026-06-19 |
| Chapter 3.1 From Linear Classifier to MLP: Why Hidden Layers Are Needed | Brench | 2026-06-22 |
| Chapter 3.2 Activation Functions: Adding Nonlinearity to Neural Networks | Brench | 2026-06-23 |