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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

Questions

  1. Why is the prediction on a translated image low when using traditional neural networks?
  2. How is convolution done?
  3. How are optimal weight values in a filter identified?
  4. How does the combination of convolution and pooling help in addressing the issue of image translation?
  5. What do the filters in layers closer to the input layer learn?
  6. What functionality does pooling have that helps in building a model?
  1. Why can't we take an input image, flatten it (as we did on the Fashion-MNIST dataset), and then train a model for real-world images?
  2. How does data augmentation help in improving image translation?
  3. In what scenario do we leverage collate_fn for DataLoaders?
  4. What impact does varying the number of training data points have on the classification accuracy of the validation dataset?
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