PyTorch Recipes: A Problem-Solution Approach

PyTorch Recipes: A Problem-Solution Approach Front Cover
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2019-02-14
184 pages

Book Description

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them.

Moving on to ; you will learn how PyTorch works with supervised and unsupervised . You will see how convolutional neural , deep neural , and recurrent neural work using PyTorch. In conclusion you will get acquainted with and text using PyTorch.

What You Will Learn

  • Master tensor operations for graph-based calculations using PyTorch
  • Create PyTorch transformations and graph computations for neural networks
  • Carry out supervised and unsupervised learning using PyTorch
  • Work with deep learning algorithms such as CNN and RNN
  • Build LSTM in PyTorch
  • Use PyTorch for text processing

Who This Book Is For

Readers wanting to dive straight into programming PyTorch.

Book Details

  • Title: PyTorch Recipes: A Problem-Solution Approach
  • Author:
  • Length: 184 pages
  • Edition: 1st ed.
  • Language: English
  • Publisher:
  • Publication Date: 2019-02-14
  • ISBN-10: 1484242572
  • ISBN-13: 9781484242575
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