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Machine learning = a journey to deep...

Wichert, Andrzej.

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Machine learning = a journey to deep learning : with exercises and answers /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning/ Andreas Wichert, Luis Sa-Couto.
其他題名:	a journey to deep learning : with exercises and answers /
作者:	Wichert, Andrzej.
其他作者:	Sa-couto, Luis.
出版者:	Singapore :World Scientific, : c2021.,
面頁冊數:	1 online resource (xvi, 624 p.)
內容註:	Preface -- Introduction -- Probability and information -- Linear algebra and optimization -- Linear and nonlinear regression -- Perceptron -- Multilayer perceptron -- Learning theory -- Model selection -- Clustering -- Radial basis networks -- Support vector machines -- Deep learning -- Convolutional networks -- Recurrent networks -- Autoencoders -- Epilogue -- Bibliography -- Index.
標題:	Machine learning. -
電子資源:	https://www.worldscientific.com/worldscibooks/10.1142/12201#t=toc
ISBN:	9789811234064

Machine learning = a journey to deep learning : with exercises and answers /
Wichert, Andrzej.

Machine learninga journey to deep learning : with exercises and answers /[electronic resource] :Andreas Wichert, Luis Sa-Couto. - Singapore :World Scientific,c2021. - 1 online resource (xvi, 624 p.)

Includes bibliographical references and index.

Preface -- Introduction -- Probability and information -- Linear algebra and optimization -- Linear and nonlinear regression -- Perceptron -- Multilayer perceptron -- Learning theory -- Model selection -- Clustering -- Radial basis networks -- Support vector machines -- Deep learning -- Convolutional networks -- Recurrent networks -- Autoencoders -- Epilogue -- Bibliography -- Index.

"This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives - the statistical perspective, the artificial neural network perspective and the deep learning methodology. The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students"--Publisher's website.

Mode of access: World Wide Web.

ISBN: 9789811234064Subjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .W53 2021

Dewey Class. No.: 006.31

Machine learning = a journey to deep learning : with exercises and answers /
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245 1 0 $a Machine learning $h [electronic resource] : $b a journey to deep learning : with exercises and answers / $c Andreas Wichert, Luis Sa-Couto.
260 $a Singapore : $b World Scientific, $c c2021.
300 $a 1 online resource (xvi, 624 p.)
504 $a Includes bibliographical references and index.
505 0 $a Preface -- Introduction -- Probability and information -- Linear algebra and optimization -- Linear and nonlinear regression -- Perceptron -- Multilayer perceptron -- Learning theory -- Model selection -- Clustering -- Radial basis networks -- Support vector machines -- Deep learning -- Convolutional networks -- Recurrent networks -- Autoencoders -- Epilogue -- Bibliography -- Index.
520 $a "This unique compendium discusses some core ideas for the development and implementation of machine learning from three different perspectives - the statistical perspective, the artificial neural network perspective and the deep learning methodology. The useful reference text represents a solid foundation in machine learning and should prepare readers to apply and understand machine learning algorithms as well as to invent new machine learning methods. It tells a story outgoing from a perceptron to deep learning highlighted with concrete examples, including exercises and answers for the students"--Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Machine learning. $3 533906
700 1 $a Sa-couto, Luis. $3 3620942
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/12201#t=toc