Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Learning Hub

Free Learning

You're reading from Modern Computer Vision with PyTorch Explore deep learning concepts and implement over 50 real-world image applications

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781839213472

Length 824 pages

Edition 1st Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Authors (2):

Yeshwanth Reddy

V Kishore Ayyadevara

View More author details

Table of Contents (25) Chapters

Preface

1. Section 1 - Fundamentals of Deep Learning for Computer Vision

2. Artificial Neural Network Fundamentals FREE CHAPTER

3. PyTorch Fundamentals

4. Building a Deep Neural Network with PyTorch

5. Section 2 - Object Classification and Detection

6. Introducing Convolutional Neural Networks

7. Transfer Learning for Image Classification

8. Practical Aspects of Image Classification

9. Basics of Object Detection

10. Advanced Object Detection

11. Image Segmentation

12. Applications of Object Detection and Segmentation

13. Section 3 - Image Manipulation

14. Autoencoders and Image Manipulation

15. Image Generation Using GANs

16. Advanced GANs to Manipulate Images

17. Section 4 - Combining Computer Vision with Other Techniques

18. Training with Minimal Data Points

19. Combining Computer Vision and NLP Techniques

20. Combining Computer Vision and Reinforcement Learning

21. Moving a Model to Production

22. Using OpenCV Utilities for Image Analysis

23. Other Books You May Enjoy

Leave a review - let other readers know what you think

Appendix

Questions

How does a region proposal technique generate proposals?
How is IoU calculated if there are multiple objects in an image?
Why does R-CNN take a long time to generate predictions?
Why is Fast R-CNN faster when compared with R-CNN?
How does RoI pooling work?
What is the impact of not having multiple layers post the feature map obtained when predicting the bounding box corrections?
Why do we have to assign a higher weight to regression loss when calculating overall loss?
How does non-max suppression work?

The rest of the chapter is locked

A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.

Unlock this book and the full library FREE for 7 days

Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of

Start free trial

Renews at $19.99/month. Cancel anytime

Authors (2)

V Kishore Ayyadevara

Kishore Ayyadevara is an entrepreneur and a hands-on leader working at the intersection of technology, data, and AI to identify and solve business problems. With over a decade of experience in leadership roles, Kishore has established and grown successful applied data science teams at American Express and Amazon, as well as a top health insurance company. In his current role, he is building a start-up focused on making AI more accessible to healthcare organizations. Outside of work, Kishore has shared his knowledge through his five books on ML/AI, is an inventor with 12 patents, and has been a speaker at multiple AI conferences.

See other products by V Kishore Ayyadevara

Yeshwanth Reddy

Yeshwanth Reddy is a highly accomplished data scientist manager with 9+ years of experience in deep learning and document analysis. He has made significant contributions to the field, including building software for end-to-end document digitization, resulting in substantial cost savings. Yeshwanth's expertise extends to developing modules in OCR, word detection, and synthetic document generation. His groundbreaking work has been recognized through multiple patents. He has also created a few Python libraries. With a passion for disrupting unsupervised and self-supervised learning, Yeshwanth is dedicated to reducing reliance on manual annotation and driving innovative solutions in the field of data science.

See other products by Yeshwanth Reddy