Essential Reads for LLM Engineers and Data Scientists

LLM Engineering offers a detailed roadmap for building, training, and deploying Large Language Models, complete with practical examples and advanced techniques, making it an essential guide for modern AI professionals

Gain a solid foundation in LangChain, agentic AI, and LangGraph, and learn to build production-ready systems with multi-agent architectures, advanced RAG pipelines, Tree of Thought reasoning, agent handoffs, and fine-grained error handling.

This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

Develop your Python machine learning skills with the latest techniques. Dive into NLP transformers, multimodal vision models, and best practices. Explore neural networks, clustering, and GPT with hands-on examples in PyTorch and TensorFlow.

A fully updated second edition that bridges NLP fundamentals with the latest in AI. New chapters on LLMs, RAG, LangChain, and autonomous agents help you build advanced language solutions with hands-on Python examples.

You will learn how to measure, visualize, and manipulate the internal components of LLMs including embeddings, transformer outputs, hidden-states, attention, and MLP layers directly.

This hands-on guide explores how to design AI agents powered by Retrieval-Augmented Generation (RAG), with cutting-edge coverage of memory systems, LangMem, and GraphRAG

Deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems with structured guidance. Gain the confidence to engage with advanced ML literature and tailor algorithms to meet your project requirements.

A code-first guide to RAG, from ingestion and chunking through hybrid retrieval, reranking, and evaluation. Written by bestselling AI author, it teaches you how to build, optimize, and debug every layer of a RAG pipeline from first principles.

Fully updated with PyTorch and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks.

This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading challenges, and manage the strategy development process from idea to backtesting and evaluation.

Learn to implement a variety of practical Python solutions for time series analysis and anomaly detection. Working through recipes, you'll be able to extract insights and create forecasts using statistical, machine learning, and deep learning models.