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Rahul Agarwal
Founder | Agentic AI... • 11h
Feeling lost with so many AI/ML books and which one to read? These are 9 curated books that actually teach you how to build, ship, and scale real AI systems. No fluff. No filler. Just actionable knowledge. 1. AI Engineering - Chip Huyen Covers everything from data pipelines to production failures. Chip has built ML systems at Netflix and NVIDIA. She knows what breaks in practice. 2. Prompt Engineering for LLMs - Ziegler & Berryman Essential for Claude or OpenAI users. Patterns, testing methods, and prompt consistency explained clearly. 3. Hands-On Large Language Models - Jay & Maarten Hugging Face, LangChain, vector databases. Everything you’ll use daily, explained in depth. 4. LLM Engineer’s Handbook - Iusztin & Labonne RAG, fine-tuning, vector DBs, plus working code. Practical knowledge you can apply immediately. 5. Building LLMs from Scratch - Sebastian Raschka From tokenization to attention layers, build GPT-style models in Python and PyTorch. No shortcuts. 6. Building LLMs for Production - Bouchard & Peters Covers scaling, inference costs, and real-world traffic. Most books stop at training; this goes further. 7. Designing ML Systems - Chip Huyen Why your model may ace benchmarks but fail users. Data drift, monitoring, reliability—everything before deployment. 8. Deep Learning - Goodfellow, Bengio & Courville Dense but foundational. Understand why tools work, not just how to use them. 9. Mathematics for Machine Learning - Deisenroth, Faisal & Ong Linear algebra, calculus, probability, all tied directly to machine learning. Pure essentials. Nine books. No extras needed. Don’t look for more recommendations, and start with these. Which one will you start with?
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