This book starts with an introduction to PyTorch and tensors, the data structure used to calculate arithmetic operations. Author Pradeepta Mishra then looks at probability distributions using PyTorch, and dives into transformations and graph computations. Along the way the book covers common issues faced with neural network implementation and tensor differentiation. Moving on to algorithms it shows how PyTorch works with supervised and unsupervised algorithms, as well as how convolutional neural networks, deep neural networks, and recurrent neural networks work using it. The book concludes with natural language processing and text processing using PyTorch.

<ASIN:1484242572>

Author: Pradeepta Mishra
Publisher: Apress
Date: January 2019
Pages: 204
ISBN: 978-1484242575
Print: 1484242572
Kindle: B07N71V7YJ
Audience: PyTorch developers
Level: Intermediate
Category: Artificial Intelligence and Python

• 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

For recommendations of Python books see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section.

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