Deep Learning is a comprehensive deep learning book covering the mathematical foundations, machine learning concepts, practical techniques, and real-world applications of modern artificial intelligence. …

Algorithms for Validation provides a rigorous and accessible introduction to algorithms for validating safety-critical and autonomous systems. The book explores computational methods for validating decision-making systems used in high-stakes fields such as autonomous vehicles, aviation, finance, and healthcare. …

Algorithms for Optimization: Second Edition is a practical, thoroughly updated guide to solving optimization problems, now expanded with new material on duality, quadratic programming, and disciplined convex programming. …

Multi-Agent Reinforcement Learning is the first complete introduction to MARL, an exciting machine learning field where multiple agents learn to interact within a shared environment. Applications span autonomous driving, multi-robot systems, automated trading, and energy management. …

An Introduction to Statistical Learning: with Applications in R is a practical and accessible guide to statistical learning, machine learning, data science, and predictive modeling. This popular textbook explains essential statistical learning methods, including linear regression, classification, resampling, regularization, decision trees, support vector machines, clustering, deep learning, survival analysis, and multiple testing. …

Hands-On Machine Learning with R offers a practical, hands-on introduction to modern machine learning techniques using R. Designed as a practitioner’s guide, the book helps readers understand the machine learning process and apply it to real-world data. …

Mathematics for Machine Learning provides a clear, self-contained introduction to the mathematical foundations behind modern machine learning. It covers essential topics including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. …

Generative AI in C++: Coding Transformers and LLMs is a practical guide for C++ developers who want to explore generative AI without getting buried in complex mathematics. The book takes you from the basics to advanced concepts, explaining how GPT-style Transformer engines and Large Language Models (LLMs) work under the hood. …

Machine Learning Systems at Scale explores the engineering challenges of building and operating modern AI systems at massive scale. It covers the full lifecycle of large-scale machine learning, from designing distributed architectures and building infrastructure to ensuring reliability and deploying services worldwide. …

Introduction to Machine Learning Systems is a practical, principle-driven textbook that explores machine learning as an engineering discipline. It teaches students and practitioners how to reason quantitatively about ML systems, from data pipelines and model development to deployment. …