Machine Learning Algorithms in Depth.

Format:

Book

Author/Creator:

Smolyakov, Vadim.

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Markov processes.

Monte Carlo method.

Machine theory.

Physical Description:

1 online resource (215 pages)

Edition:

1st ed.

Place of Publication:

New York : Manning Publications Co. LLC, 2024.

Summary:

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.

Contents:

Part 1. Introducing ML algorithms. Machine learning algorithms

Markov chain Monte Carlo

Variational inference

Software implementation

Part 2. Supervised learning. Classification algorithms

Regression algorithms

Selected supervised learning algorithms

Part 3. Unsupervised learning. Fundamental unsupervised learning algorithms

Selected unsupervised learning algorithms

Part 4. Deep learning. Fundamental deep learning algorithms

Advanced deep pearning algorithms.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

9781638355571

1638355576

9781633439214

1633439216

OCLC:

1492940765