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Deep Learning with PyTorch

You're reading from Deep Learning with PyTorch

Product type Book

Published in Feb 2018

Publisher Packt

ISBN-13 9781788624336

Pages 262 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Vishnu Subramanian

Table of Contents (11) Chapters

Preface

1. Getting Started with Deep Learning Using PyTorch

2. Building Blocks of Neural Networks

3. Diving Deep into Neural Networks

4. Fundamentals of Machine Learning

5. Deep Learning for Computer Vision

6. Deep Learning with Sequence Data and Text

7. Generative Networks

8. Modern Network Architectures

9. What Next?

10. Other Books You May Enjoy

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Installing PyTorch

PyTorch is available as a Python package and you can either use pip, or conda, to build it or you can build it from source. The recommended approach for this book is to use the Anaconda Python 3 distribution. To install Anaconda, please refer to the Anaconda official documentation at https://conda.io/docs/user-guide/install/index.html. All the examples will be available as Jupyter Notebooks in the book's GitHub repository. I would strongly recommend you use Jupyter Notebook, since it allows you to experiment interactively. If you already have Anaconda Python installed, then you can proceed with the following steps for PyTorch installation.

For GPU-based installation with Cuda 8:

conda install pytorch torchvision cuda80 -c soumith

For GPU-based installation with Cuda 7.5:

conda install pytorch torchvision -c soumith

For non-GPU-based installation:

conda...

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Authors (1)

Vishnu Subramanian

Vishnu Subramanian has an experience in leading, architecting and implementing several Big Data analytical projects (AI, ML and Deep Learning). Specialized in Machine learning, Deep Learning, Distributed ML, and Visualization. Also, he has experience in domains like Retail, Finance, and Travel and has specialized in understanding and coordinating between Business, AI (Machine Learning, Deep learning) and Engineering teams.

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