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Reinforcement Learning, second edition: An Introduction

Admin — Mon, 19 Oct 2026 06:55:47 +0000

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 topics such as temporal-difference learning, Monte Carlo methods, function approximation, neural networks, and policy-gradient methods. It also includes real-world …

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Distributional Reinforcement Learning

Admin — Sun, 11 Oct 2026 15:45:39 +0000

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 distribution of future rewards rather than relying solely on expected values. Covering essential mathematical foundations, key algorithms, and real-world applications, …

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Deep Learning

Admin — Thu, 24 Sep 2026 10:42:26 +0000

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 feedforward networks, convolutional neural networks, sequence modeling, optimization, regularization, and representation learning. It also covers applications in natural language processing, …

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Algorithms for Validation

Admin — Mon, 21 Sep 2026 05:37:43 +0000

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. It explains the mathematical foundations behind validation problems and presents practical algorithms for analyzing complex system behaviors …

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Algorithms for Optimization: Second Edition

Admin — Fri, 18 Sep 2026 11:51:44 +0000

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 engineering-minded approach, focusing on designing systems that optimize real metrics under real-world constraints. You’ll explore techniques for navigating high-dimensional …

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Multi-Agent Reinforcement Learning

Admin — Wed, 16 Sep 2026 17:45:24 +0000

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. Written by leading experts, the book blends reinforcement learning, deep learning, and game theory into clear, accessible explanations of …

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An Introduction to Statistical Learning: with Applications in R

Admin — Tue, 08 Sep 2026 12:45:53 +0000

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. With clear explanations, …

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Hands-On Machine Learning with R

Admin — Sun, 06 Sep 2026 03:10:54 +0000

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 packages such as glmnet, h2o, ranger, xgboost, and keras, showing how to build models, …

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Mathematics for Machine Learning

Admin — Tue, 01 Sep 2026 04:54:09 +0000

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. The book connects these concepts to four important machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support …

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Generative AI in C++: Coding Transformers and LLMs

Admin — Sun, 30 Aug 2026 10:01:01 +0000

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. It includes C++ source …

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