I write practical, research-backed books on AI and machine learning. Trusted by thousands of practitioners worldwide and highly rated on Amazon.
The Seminal Papers
Principles for architecting reliable and verifiable AI. A curated deep-dive into the foundational research papers that shaped RAG, with practical commentary and implementation guidance.
2nd Edition
Build production-ready LLM applications and advanced agents using Python and LangGraph. The go-to guide for developers building real-world generative AI systems.
1st Edition
Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods. Covers classical techniques through deep learning approaches.
Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6. Practical, hands-on solutions for real AI challenges.
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Most RAG tutorials stop at the first plausible answer. This lab starts there. On a travel-company case study you build a RAG system on small open models, expose its failure modes on purpose, fix retrieval with hybrid search and reranking, add corrective retrieval and answer self-checking, benchmark quality against latency and cost, then put open guardrail models in front of the finished app. It runs without a paid API key, a GPU cluster, or a per-token bill.
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