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Deep Learning with PyTorch

You're reading from Deep Learning with PyTorch A practical approach to building neural network models using PyTorch

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788624336

Length 262 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Deep Learning

Author (1):

Vishnu Subramanian

View More author details

Table of Contents (11) Chapters

Preface

1. Getting Started with Deep Learning Using PyTorch

2. Building Blocks of Neural Networks FREE CHAPTER

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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Summary

In this introductory chapter, we explored what artificial intelligence, machine learning, and deep learning are and we discussed the differences between all the three. We also looked at applications powered by them in our day-to-day lives. We dig deeper into why DL is only now becoming more popular. Finally, we gave a gentle introduction to PyTorch, which is a deep learning framework.

In the next chapter, we will train our first neural network in PyTorch.

The rest of the chapter is locked

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Authors (1)

Vishnu Subramanian

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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