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Modern Computer Vision with PyTorch

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

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Length 824 pages
Edition 1st Edition
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Authors (2):
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Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
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Toc

Table of Contents (25) Chapters Close

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 Appendix

Chapter 3 - Building a Deep Neural Network with PyTorch

  1. What is the issue if the input values are not scaled in the input dataset?
    It takes longer to adjust weights to optimal value because input values vary so widely when they are unscaled
  2. What could be the issue if the background has a white pixel color while the content has a black pixel color when training a neural network?
    The neural network has to learn to ignore a majority of the not so useful content that is white in color
  3. What is the impact of batch size on the model's training time, accuracy over a given number of epochs?
    The larger the batch size more is the time taken to converge and more iterations required to attain a high accuracy
  4. What is the impact of the input value range on weight distribution at the end of the training?
    If the input value is not scaled to a certain range, certain weights can aid in over-fitting
  5. How does batch normalization help in improving accuracy?
    Just like how it is important that we scale...
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