Part I: Foundations
1. Neural Networks Without the Magic
Why metaphors aren't enough. What this book is, who it's for, and what you'll build by the end.
2. History of Neural Networks
The failures and breakthroughs that shaped the field.
3. Rust for Neural Networks
Rust's explicitness as a teaching tool.
4. Tensors: The Foundation
The fundamental data structure of deep learning.
Part II: Building Blocks
5. Building Your First Neural Network
A complete implementation from scratch.
6. Loss Functions and Optimizers
How networks measure wrongness and minimize it.
7. Backpropagation from Scratch
The algorithm that makes learning possible. Implement it yourself,
8. Activation Functions
The non-linearity that enables complex patterns.
9. The Learning Rate
The most important hyperparameter. Too high and you overshoot; too low and you stall.
Part III: Architectures
10. Convolutional Neural Networks
Image processing through sliding windows of multiplication.
11. Recurrent Neural Networks
Processing sequences with memory. The Elman architecture
12. LSTM: Next Token Prediction
Building a character-level language model. Predict the next letter, generate text.
Part IV: Transformers
13. Tokenizers
Turning text into numbers. Byte-pair encoding, the bridge between words and vectors.
14. Token Embeddings
Turning numbers into vectors. How meaning gets encoded
15. Self-Attention
The core mechanism of modern AI. Queries, keys, values—attention as matrix
16. Transformer
Putting it all together. Build a complete transformer
Appendices
Appendix A: Pretrained Models in Candle
Using Hugging Face models without starting from scratch.
Appendix B: Tensor Shape Error Reference
Debugging the most common mistakes. Shape mismatches decoded and resolved.
Appendix C: Inference Optimizations
Making models faster on laptops. Practical speedups without a GPU.
