Modern Computer Vision with PyTorch - Second Edition
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Modern Computer Vision with PyTorch - Second Edition

A practical roadmap from deep learning fundamentals to advanced applications and Generative AI

About the Book

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks.

The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion.

You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production.

By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. Artificial Neural Network Fundamentals
  2. PyTorch Fundamentals
  3. Building a Deep Neural Network with PyTorch
  4. Introducing Convolutional Neural Networks
  5. Transfer Learning for Image Classification
  6. Practical Aspects of Image Classification
  7. Basics of Object Detection
  8. Advanced Object Detection
  9. Image Segmentation
  10. Applications of Object Detection and Segmentation
  11. Autoencoders and Image Manipulation
  12. Image Generation Using GANs
  13. Advanced GANs to Manipulate Images
  14. Combining Computer Vision and Reinforcement Learning
  15. Combining Computer Vision and NLP Techniques
  16. Foundation Models in Computer Vision
  17. Applications of Stable Diffusion
  18. Moving a Model to Production
  19. Appendix

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