PyTorch Recipes: A Problem-Solution Approach

PyTorch Recipes: A Problem-Solution Approach Front Cover
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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 computations with PyTorch. Along the way you will take a look at common issues faced with neural 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 networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language 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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