In this book Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students.
Author: Francis Bach Publisher: The MIT Press Date: December 2024 Pages: 496 ISBN: 978-0262049443 Print: 0262049449 Kindle: B0CYZNZ55J Audience: General Level: Intermediate/Advanced Category: Methodology

- Provides a balanced and unified treatment of most prevalent machine learning methods
- Emphasizes practical application and features only commonly used algorithmic frameworks
- Covers modern topics not found in existing texts, such as overparameterized models and structured prediction
- Integrates coverage of statistical theory, optimization theory, and approximation theory
- Focuses on adaptivity, allowing distinctions between various learning techniques
- Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors
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Object-Oriented Python
Author: Irv Kalb Publisher: No Starch Press Date: January 2022 Pages: 416 ISBN: 978-1718502062 Print: 1718502060 Kindle: B0957SHYQL Audience: Python developers Rating: 3 Reviewer: Mike James Python, Object-Oriented? Not a lot of programmers know that!
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Continuous Architecture In Practice (Addison-Wesley)
Author: Murat Erder, Pierre Pureur and Eoin Woods Publisher: Addison-Wesley Pages: 352 ISBN: 978-0136523567 Print: 0136523560 Kindle: B08ZRTQGLJ Audience: Software Architects Rating: 3 Reviewer: Kay Ewbank
This book sets out the case for why software architecture is more important than ever, and in p [ ... ]
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