From Zero to Agents: A Foundational AI/ML Course, Built First Principles

Most machine learning books ask you to trust the black box. This one doesn't. From Zero to Agents rebuilds artificial intelligence from first principles - starting at the question of what a word even is to a computer, and ending with a working, tool-using AI agent you build yourself. Every concept is explained three ways: theoretically, mathematically, and in code - first written from scratch in raw Python, then reimplemented in PyTorch, so you always know exactly what the library is doing for you. This is not a tutorial that skips the hard parts. Every equation is derived. Every result is verified by running the code - including the places where the first attempt didn't work, and the book says so. Inside, you'll build: - Tokenization and embeddings, from raw text to trainable vectors- The mathematical toolkit - vectors, matrices, gradients, probability - explained through working code, not abstract proofs- A complete neural network, with backpropagation derived and verified by hand- Recurrent networks, LSTMs, and a full transformer with real multi-head attention, trained from scratch- LoRA fine-tuning, derived and measured, not just described- A working AI agent - tool use, memory, multi-agent coordination - arriving at the book's own title Written for practitioners who want to open a real AI/ML research paper and read the mathematics fluently, on sight - not skim past it to the prose.