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Deep learning : = a practical introduction /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep learning :/ Manel Martinez-Ramón, Meenu Ajith, Aswathy Rajendra Kurup.
其他題名:	a practical introduction /
作者:	Martínez-Ramón, Manel,
其他作者:	Kurup, Aswathy Rajendra.
出版者:	Hoboken, NJ :Wiley, : c2024.,
面頁冊數:	xxiii, 392 p. :ill. ; : 26 cm.;
標題:	Deep learning (Machine learning) -
ISBN:	9781119861867 (hbk.) :

Deep learning : = a practical introduction /
Martínez-Ramón, Manel,1968-

Deep learning :a practical introduction /Manel Martinez-Ramón, Meenu Ajith, Aswathy Rajendra Kurup. - Hoboken, NJ :Wiley,c2024. - xxiii, 392 p. :ill. ;26 cm.

In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools; Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures; Practical discussions of recurrent neural networks and non-supervised approaches to deep learning; Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks.

ISBN: 9781119861867 (hbk.) :NT3537Subjects--Topical Terms:

1381171
Deep learning (Machine learning)

LC Class. No.: Q325.73 / .M37 2024

Dewey Class. No.: 006.3/1 / M385

Deep learning : = a practical introduction /
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520 # $a In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools; Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures; Practical discussions of recurrent neural networks and non-supervised approaches to deep learning; Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks.
650 # 0 $a Deep learning (Machine learning) $3 1381171
700 1 # $a Kurup, Aswathy Rajendra. $3 1451829
700 1 # $a Ajith, Meenu. $3 1451828