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Pytorch Recipes eBook

A Problem-Solution Approach

by Pradeepta Mishra
language: english
Publisher: Apress, January of 2019 ‧
52,99€
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Ebook for ADE
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 algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. 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 processing and text processing using PyTorch.

What You Will Learn
  • Master tensor operations for dynamic 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 models in PyTorch 
  • Use PyTorch for text processing 
Who This Book Is For

Readers wanting to dive straight into programming PyTorch.

Pytorch Recipes

A Problem-Solution Approach

by Pradeepta Mishra

Property Description
ISBN: 9781484242582
Publisher: Apress
Release Date: January of 2019
Language: English
Format: eBook
File Format and Compatibility:
Collection: Professional And Applied Computing
Categories: eBooks in English > Management > Management and Organization
eBooks in English > Computing > Schedule
eBooks in English > Computing > Database
EAN: 9781484242582
Acessibilidade: Ver características de acessibilidade indicadas pelo editor

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