國立虎尾科技大學 |

Machine learning fundamentals = a concise introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine learning fundamentals/ Hui Jiang, York University, Toronto.
其他題名:	a concise introduction /
作者:	Jiang, Hui.
出版者:	Cambridge :Cambridge University Press, : 2021.,
面頁冊數:	xviii, 380 p. :ill. (some col.), digital ; : 27 cm.;
附註:	Title from publisher's bibliographic system (viewed on 26 Nov 2021).
標題:	Machine learning. -
電子資源:	https://doi.org/10.1017/9781108938051
ISBN:	9781108938051

Machine learning fundamentals = a concise introduction /
Jiang, Hui.

Machine learning fundamentalsa concise introduction /[electronic resource] :Hui Jiang, York University, Toronto. - Cambridge :Cambridge University Press,2021. - xviii, 380 p. :ill. (some col.), digital ;27 cm.

Title from publisher's bibliographic system (viewed on 26 Nov 2021).

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.

ISBN: 9781108938051Subjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5 / .J53 2021

Dewey Class. No.: 006.31

Machine learning fundamentals = a concise introduction /
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500 $a Title from publisher's bibliographic system (viewed on 26 Nov 2021).
520 $a 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.
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