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PyTorch from Scratch: Volume 1: Master Deep Learning Step by Step
Learn deep learning from the ground up - no math prerequisites, no skipped steps, no hand-waving.
PyTorch from Scratch, Volume 1 takes you from your very first tensor to building Transformer models, one concept at a time. Every formula is derived step by step with color-coded arithmetic so you can trace exactly where each number comes from. Every code block runs. Every architecture is built from scratch before you use the PyTorch shortcut.
What you will
Part I - Foundations (Chapters 1–3)
Install PyTorch, master tensors as the universal data structure, and understand autograd - the engine that computes gradients automatically so you never write a derivative by hand.
Part II - Building Neural Networks (Chapters 4–7)
Construct models with nn.Module. Write training loops that actually converge. Understand why models overfit, how regularization tames them, and how to feed data efficiently with Dataset and DataLoader.
Part III - Computer Vision (Chapters 8–9)
Build convolutional neural networks from the sliding-window intuition through residual connections, batch normalization, and depthwise separable convolutions. Then use transfer learning to adapt a pretrained model to your own data with minimal training.
Part IV - Sequence Models and Attention (Chapters 10–11)
Recurrent networks and LSTMs for sequential data. The complete Transformer architecture - self-attention, multi-head attention, positional encoding - built up from first principles with worked examples you can follow on paper.
What makes this book
• Every mathematical derivation shows ALL intermediate steps - nothing is left as "an exercise for the reader."
• 80+ diagrams visualize tensor operations, network architectures, and gradient flow
• Scenario-based Knowledge Check questions modeled on real interview questions at major tech companies
• Hands-on exercises graded from beginner to challenging at the end of every chapter
• Appendices cover Python, linear algebra, and calculus prerequisites - No prior math knowledge assumed
Who this book is
Software engineers, data scientists, and students who know Python and want to learn deep learning properly - not just copy code, but understand what every line does and why.
If you have used NumPy or pandas, PyTorch tensors will feel immediately familiar.
Volume 1 is self-contained. By Chapter 11 you will have built, trained, and understood every core neural network architecture used in modern AI - feed-forward networks, CNNs, RNNs, LSTMs, and Transformers.
Volume 2 covers deployment, GPU acceleration, debugging, and fine-tuning large language models with LoRA.
PyTorch from Scratch, Volume 1 takes you from your very first tensor to building Transformer models, one concept at a time. Every formula is derived step by step with color-coded arithmetic so you can trace exactly where each number comes from. Every code block runs. Every architecture is built from scratch before you use the PyTorch shortcut.
What you will
Part I - Foundations (Chapters 1–3)
Install PyTorch, master tensors as the universal data structure, and understand autograd - the engine that computes gradients automatically so you never write a derivative by hand.
Part II - Building Neural Networks (Chapters 4–7)
Construct models with nn.Module. Write training loops that actually converge. Understand why models overfit, how regularization tames them, and how to feed data efficiently with Dataset and DataLoader.
Part III - Computer Vision (Chapters 8–9)
Build convolutional neural networks from the sliding-window intuition through residual connections, batch normalization, and depthwise separable convolutions. Then use transfer learning to adapt a pretrained model to your own data with minimal training.
Part IV - Sequence Models and Attention (Chapters 10–11)
Recurrent networks and LSTMs for sequential data. The complete Transformer architecture - self-attention, multi-head attention, positional encoding - built up from first principles with worked examples you can follow on paper.
What makes this book
• Every mathematical derivation shows ALL intermediate steps - nothing is left as "an exercise for the reader."
• 80+ diagrams visualize tensor operations, network architectures, and gradient flow
• Scenario-based Knowledge Check questions modeled on real interview questions at major tech companies
• Hands-on exercises graded from beginner to challenging at the end of every chapter
• Appendices cover Python, linear algebra, and calculus prerequisites - No prior math knowledge assumed
Who this book is
Software engineers, data scientists, and students who know Python and want to learn deep learning properly - not just copy code, but understand what every line does and why.
If you have used NumPy or pandas, PyTorch tensors will feel immediately familiar.
Volume 1 is self-contained. By Chapter 11 you will have built, trained, and understood every core neural network architecture used in modern AI - feed-forward networks, CNNs, RNNs, LSTMs, and Transformers.
Volume 2 covers deployment, GPU acceleration, debugging, and fine-tuning large language models with LoRA.
595 pages, Paperback
Published April 19, 2026
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