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Develop a mathematical intuition for how machine learning algorithms work so you can improve model performance and effectively troubleshoot complex ML problems.
Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probability-based algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning.
For each category of algorithm, you’ll go from math-first principles to a hands-on implementation in Python, exploring dozens of examples from across all the fields of machine learning. Each example is accompanied by worked-out derivations and details, as well as insightful code samples and graphics.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
Machine Learning Algorithms in Depth
by Vadim SmolyakovFormat: Nonspecific Binding
Pub. Date: 2025-02-18
Publisher(s): Simon & Schuster
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Summary
Author Biography
Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. He is a former PhD student in AI at MIT CSAIL with research interests in Bayesian inference and deep learning. Prior to joining Microsoft, Vadim developed machine learning solutions in the e-commerce space.
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