Overview
- Adopts a problem-solution approach to PyTorch programming
- Includes deep learning algorithms with PyTorch
- Covers natural language processing and text processing
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (7 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: PyTorch Recipes
Book Subtitle: A Problem-Solution Approach
Authors: Pradeepta Mishra
DOI: https://doi.org/10.1007/978-1-4842-4258-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Pradeepta Mishra 2019
eBook ISBN: 978-1-4842-4258-2Published: 28 January 2019
Edition Number: 1
Number of Pages: XX, 184
Number of Illustrations: 280 b/w illustrations
Topics: Python, Big Data, Big Data/Analytics