Reading List

Reading List

Books I want to read.

  • Reinforcement Learning from Human Feedback — Manning (via AI Book Club)
  • AI Product Manager’s Handbook (2nd ed.) — Irene Bratsis, Packt (building/scaling AI products, AI-native vs. evolving products, commercialization)
  • Machine Learning Platform Engineering — Benjamin Tan Wei Hao, Shanoop Padmanabhan, Varun Mallya, Manning (build an MLOps/LLMOps platform from scratch: Kubeflow, MLflow, BentoML, Feast, model serving & monitoring)
  • Machine Learning System Design — Valerii Babushkin, Arseny Kravchenko, Manning (end-to-end ML system framework: problem framing, dataset gathering, training pipelines, serving & monitoring)
  • Knowledge Graphs and LLMs in Action — Giuseppe Futia, Vlastimil Kus, Manning (building knowledge graphs from structured/unstructured sources, integrating with LLM apps & RAG pipelines)
  • Graph Neural Networks in Action — Keita Broadwater, Manning (building GNNs in Python for node prediction, link prediction, graph classification)
  • Introduction to Machine Learning — Laurent Younes, arXiv textbook (mathematical foundations of ML: linear algebra/probability, kernel methods, supervised & generative learning, generalization theory)
  • The Little Book of Deep Learning — François Fleuret (compact free ebook: gradient descent, backprop, model components, architectures, applications)
  • Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory — Kuckuck et al., arXiv (737pp: ANN architectures, optimization theory, approximation/generalization theory, deep learning for PDEs)
  • LLM Customization and Fine-Tuning — Amit Bahree, Weehyong Tok, Manning (adaptation spectrum from prompting/RAG through LoRA/QLoRA, full SFT, distillation, and DPO alignment; production ops for drift & safety)
  • A First Course in Causal Inference — Peng Ding, UC Berkeley lecture notes (causal inference from basic probability, statistical inference, linear/logistic regression)
  • AI Agents and Applications: With LangChain, LangGraph, and MCP — Roberto Infante, Manning (build LLM-powered agentic applications: agent workflows, tools, MCP integrations)
  • Multi-Agent AI Engineering: Design, build, and operate AI systems that think and act as coordinated teams — Dr. Xiao Ma, Dr. Chi Wang, Packt (production-grade multi-agent systems: communication protocols, memory/context, orchestration, evaluation, security, observability)
  • AI Agents in Action (2nd ed.) — Micheal Lanham, Manning (autonomous agent design/deployment, MCP tools/memory, reasoning & planning patterns — ReAct, Reflexion, Tree-of-Thought, multi-agent patterns)

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