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Fairness and Machine Learning
Fairness and Machine Learning is a comprehensive textbook exploring fairness, bias, and discrimination in artificial intelligence and automated decision-making. Designed for advanced undergraduate and graduate students, this free machine learning book covers statistical and…
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Think Python, 3rd edition
Think Python: How to Think Like a Computer Scientist, 3rd edition is a free Python programming book for beginners looking to learn Python from scratch. This comprehensive Python textbook covers essential programming concepts, including variables, functions, data structures,…
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Reinforcement Learning, second edition: An Introduction
Reinforcement Learning, second edition: An Introduction by Richard S. Sutton and Andrew G. Barto is a comprehensive textbook covering the fundamental concepts, algorithms, and applications of reinforcement learning in artificial intelligence. This updated edition explores key…
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Distributional Reinforcement Learning
Distributional Reinforcement Learning is a comprehensive machine learning textbook by Marc G. Bellemare, Will Dabney, and Mark Rowland. This book introduces distributional reinforcement learning, a powerful approach to AI decision-making that models the full probability…
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Deep Learning
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. This free to read deep learning book explores neural networks, deep…
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Algorithms for Validation
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,…
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Algorithms for Optimization: Second Edition
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. Authors Mykel Kochenderfer and Tim Wheeler take an…
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Multi-Agent Reinforcement Learning
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…
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An Introduction to Statistical Learning: with Applications in R
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…
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Hands-On Machine Learning with R
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. It covers popular R…