Название: Machine Learning Algorithms in Depth (Final Release)Автор: Vadim Smolyakov
Издательство: Manning Publications
Год: 2024
Страниц: 328
Язык: английский
Формат: pdf (true)
Размер: 26.6 MB
Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Fully understanding how Machine Learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you'll explore practical implementations of dozens of ML algorithms. 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 probabilistic algorithms, you'll learn the fundamentals of Bayesian inference and Deep Learning. You'll also explore the core data structures and algorithmic paradigms for Machine Learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they're put into action. Learn how Machine Learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You'll especially appreciate author Vadim Smolyakov's clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. This book was written for anyone interested in exploring Machine Learning algorithms in depth. The prerequisites for reading this book include a basic level of programming skills in Python, and an intermediate level of understanding of linear algebra, applied probability, and multivariable calculus.
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