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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 (18) Chapters

Title Page

Copyright and Credits

Dedication

Packt Upsell

Foreword

Contributors

Preface

1. Getting Started with Deep Learning Using PyTorch FREE CHAPTER

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?

1. Other Books You May Enjoy

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Index

Interesting ideas to explore

Most of the concepts that we learned in the book form the foundation of modern applications that are powered by deep learning. In this section, we will look at the different interesting projects that we can do that are related to computer vision and natural language processing (NLP).

Object detection

All the examples we have seen in this book help you in detecting whether a given image is this (cat) or that (dog). But, to solve some of the problems in the real world, you may need to identify different objects in an image, such as shown here:

Output of object detection algorithm

This image shows the output of an object detection algorithm where the algorithm is detecting objects such as a beautiful dog and cat . Just as there are off-the-shelf algorithms for image classification, there are a bunch of amazing algorithms that can help in building object recognition systems. Here is a list of some of the important algorithms and the papers that mention them:

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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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