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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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Machine Learning Systems at Scale

Admin — Sat, 29 Aug 2026 13:08:31 +0000

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. The book also examines the practical challenges of operating systems that serve millions …

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Introduction to Machine Learning Systems

Admin — Sat, 29 Aug 2026 13:08:12 +0000

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. Rather than focusing on specific tools or frameworks, the book introduces fundamental principles and quantitative methods for …

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Understanding Deep Learning

Admin — Thu, 27 Aug 2026 19:47:44 +0000

Understanding Deep Learning offers a clear, accessible, and up-to-date introduction to one of the most important fields in modern technology. Covering everything from the fundamentals of machine learning to advanced deep learning models and recent developments, the book focuses on the ideas that matter most without overwhelming readers with unnecessary technical detail. Simon Prince explains …

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Foundations of Machine Learning – Second Edition

Admin — Fri, 17 Jul 2026 05:42:59 +0000

Foundations of Machine Learning provides a comprehensive introduction to machine learning, suitable as a graduate level textbook or as a reference book for researchers. The book develops the foundations of theory and conceptual tools necessary to understand and justify machine learning algorithms, while covering the core concepts and modern topics in the field. It also …

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