Deep Learning with PyTorch
Comprehensive Notes on Deep Learning, Tensor Internals, Computer Vision, Transformers, and Production Deployment
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These notes document a systematic, first-principles study of Deep Learning with PyTorch (2nd Edition, Manning) by Eli Stevens, Luca Antiga, Thomas Viehmann, and Howard Huang.
Manning Publications
Luca Antiga, Eli Stevens, Thomas Viehmann, Howard Huang
Book & Note Structure
| Part | Focus & Scope | Core Contents |
|---|---|---|
| Part 1: Core PyTorch | Framework Mechanics & Low-Level Foundations | Tensors, physical 1D storage buffers, strides, autograd DAG engine, modular nn.Module design, datasets/dataloaders, and 2D/3D convolutions. |
| Part 2: Practical Applications | Advanced Vision, NLP & Systems Engineering | Vision Transformers (ViT), Diffusion Models (DDPM), 3D Volumetric CT Scan Cancer Detection, SAM Fine-Tuning, Multi-GPU Parallelisms (FSDP/TP/PP), and Production Deployment (torch.compile, LibTorch C++, ExecuTorch). |
— emreaslan —