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.

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Spis treści

Modern Computer Vision with PyTorch. A practical roadmap from deep learning fundamentals to advanced applications and Generative AI - Second Edition eBook -- spis treści

• 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