Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

Arrow left icon
Product type Paperback
Published in Jun 2024
Publisher Packt
ISBN-13 9781803231334
Length 746 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
Arrow right icon
View More author details
Toc

Table of Contents (26) 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. Combining Computer Vision and Reinforcement Learning 19. Combining Computer Vision and NLP Techniques 20. Foundation Models in Computer Vision 21. Applications of Stable Diffusion 22. Moving a Model to Production 23. Other Books You May Enjoy
24. Index
Appendix

Questions

  1. What are the various layers in a neural network?
  2. What is the output of feedforward propagation?
  3. How is the loss function of a continuous dependent variable different from that of a binary dependent variable or a categorical dependent variable?
  4. What is stochastic gradient descent?
  5. What does a backpropagation exercise do?
  6. How does the update of all the weights across layers happen during backpropagation?
  7. Which functions are used within each epoch of training a neural network?
  8. Why is training a network on a GPU faster when compared to training it on a CPU?
  9. What is the impact of the learning rate when training a neural network?
  10. What is the typical value of the learning rate parameter?

Learn more on Discord

Join our community’s Discord space for discussions with the authors and other readers:

https://packt.link/modcv

You have been reading a chapter from
Modern Computer Vision with PyTorch - Second Edition
Published in: Jun 2024
Publisher: Packt
ISBN-13: 9781803231334
Register for a free Packt account to unlock a world of extra content!
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
Renews at $19.99/month. Cancel anytime