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PyTorch deep learning hands-on :appl...

Passi, Sudhanshu.

PyTorch deep learning hands-on :apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.31
書名/作者:	PyTorch deep learning hands-on : : apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more // Sherin Thomas with Sudhanshu Passi.
作者:	Thomas, Sherin.
其他作者:	Passi, Sudhanshu.
出版者:	Birmingham : : Packt Pub.,, 2019.
面頁冊數:	x, 232 p. : : ill. ;; 24 cm.
標題:	Python (Computer program language)
標題:	Machine learning.
標題:	Neural networks (Computer science)
ISBN:	9781788834131 (pbk.) :
書目註:	Includes bibliographical references and index.
摘要、提要註:	Implement every major architecture of deep learning in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language (RNN), GANs, and reinforcement learning. It covers deep learning workflows, migrating models to TorchScript, and deploying to efficient production environments.

PyTorch deep learning hands-on :apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more /
Thomas, Sherin.

PyTorch deep learning hands-on :apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more /Sherin Thomas with Sudhanshu Passi. - Birmingham :Packt Pub.,2019. - x, 232 p. :ill. ;24 cm. - Expert insight. - Expert insight..

Includes bibliographical references and index.

Implement every major architecture of deep learning in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language (RNN), GANs, and reinforcement learning. It covers deep learning workflows, migrating models to TorchScript, and deploying to efficient production environments.

ISBN: 9781788834131 (pbk.) :NTD 1,240Subjects--Topical Terms:

339754
Python (Computer program language)

Dewey Class. No.: 006.31

PyTorch deep learning hands-on :apply modern AI techniques with CNNs, RNNs, GANs, reinforcement learning, and more /
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520 $a Implement every major architecture of deep learning in PyTorch. Starting with simple neural networks, it covers PyTorch for computer vision (CNN), natural language (RNN), GANs, and reinforcement learning. It covers deep learning workflows, migrating models to TorchScript, and deploying to efficient production environments.
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