Machine Learning Fundamentals - A Concise Introduction (Paperback, New edition)

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	November 2021
Authors:	Hui Jiang
Dimensions:	253 x 204 x 24mm (L x W x T)
Format:	Paperback - Trade
Pages:	418
Edition:	New edition
ISBN-13:	978-1-108-94002-3
Categories:	Books > Reference & Interdisciplinary > Communication studies > Information theory > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	1-108-94002-1
Barcode:	9781108940023

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