No fluff, no AI hype. Each book walks you end-to-end through a concrete project, the prompts, the pitfalls, and the shipped result.
Build a real mobile app, even if you've never written a line of code. 24 chapters and 2 appendices, from finding an idea to publishing on the App Store and Google Play.
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Take your agent from a working demo to a production system you can trust. Orchestration, evals, observability, cost, security, rollout, built end to end on a real SRE agent.
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The standard practical entry to modern ML. Build real models from chapter one, no math-first detour.
How ML actually runs in production: data pipelines, infra, monitoring, retraining. The systems half of the picture.
Working with LLMs as engineering substrate, prompting, RAG, evaluation, deployment. The current-state textbook.
Write a working transformer LLM in PyTorch, line by line. The clearest path to actually understanding the thing.
From the author of The Illustrated Transformer. Visual, intuitive, code-first embeddings, RAG, fine-tuning.
The Keras creator's own book. Still the best introduction to deep learning fundamentals you can buy.
Exactly what it says. The shortest serious overview of the field that doesn't cut corners. Read it in a weekend.
The reference textbook. Dense, mathematical, definitive. For when you want first principles, not a tutorial.
