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Programming PyTorch for deep learning : creating and deploying deep learning applications / Ian Pointer.

By: Material type: TextPublication details: India: Shroff Publishers and Distributors Pvt.Ltd., 2019Edition: First editionDescription: xv, 200 pages : illustrations ; 24 cmISBN:
  • 9781492045359
  • 9789352139095
Subject(s): DDC classification:
  • 006.32 23 P751
Contents:
Getting started with PyTorch -- Image classification with PyTorch -- Convolutional neural networks -- Transfer learning and other tricks -- Text classification -- A journey into sound -- Debugging PyTorch models -- PyTorch in production -- PyTorch in the wild.
Summary: "Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production."--
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Includes bibliographical references and index.

Getting started with PyTorch -- Image classification with PyTorch -- Convolutional neural networks -- Transfer learning and other tricks -- Text classification -- A journey into sound -- Debugging PyTorch models -- PyTorch in production -- PyTorch in the wild.

"Take the next steps toward mastering deep learning, the machine learning method that's transforming the world around us by the second. In this practical book, you'll get up to speed on key ideas using Facebook's open source PyTorch framework and gain the latest skills you need to create your very own neural networks. Ian Pointer shows you how to set up PyTorch on a cloud-based environment, then walks you through the creation of neural architectures that facilitate operations on images, sound, text, and more through deep dives into each element. He also covers the critical concepts of applying transfer learning to images, debugging models, and PyTorch in production."--

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