Machine Learning Algorithms in Depth

• Verfasst von: Smolyakov, Vadim [VerfasserIn]
• Titel: Machine Learning Algorithms in Depth
• Verf.angabe: Vadim Smolyakov
• Verlagsort: Shelter Island
• Verlag: Manning Publications
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: xix, 305 Seiten
• Illustrationen: Illustrationen
• ISBN: 978-1-63343-921-4
• Schlagwörter: (s)Maschinelles Lernen / (s)Algorithmus
• Dokumenttyp: Lehrbuch
• Sprache: eng
• Sach-SW: Algorithmen und Datenstrukturen
• Sach-SW: Algorithms & data structures
• Sach-SW: COM094000
• Sach-SW: COMPUTERS / Programming / Algorithms
• Sach-SW: Machine learning
• Sach-SW: Maschinelles Lernen
• Sach-SW: Apprentissage automatique
• Sach-SW: Algorithmes
• Sach-SW: Processus de Markov
• Sach-SW: Méthode de Monte-Carlo
• Sach-SW: Théorie des automates
• Sach-SW: algorithms
• K10plus-PPN: 1873096445

Abstract

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 will learn the fundamentals of Bayesian inference and deep learning

Abstract

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