Deep Learning for Image Recognition provides a detailed explanation of the fundamental theories underpinning image recognition and code for recognition tasks in specific application scenarios. Readers can manipulate the existing code, thereby deepening their understanding. Chapters include project work enabling readers to apply the skills and knowledge gained from that section of the book. Projects are based on the accessible Pytorch framework, which is straightforward to learn and can be replicated and modified. Readers are presented with current research findings and up to date techniques in image recognition and deep learning.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Fundamentals of Neural Networks and Convolutional Neural Networks - p. 1 7. Chapter 2. Fundamentals of Deep Learning Optimization Techniques - p. 33 8. Chapter 3. Data Usage in Deep Learning - p. 89 9. Chapter 4. Image Classification - p. 157 10. Chapter 5. Object Detection - p. 221 11. Chapter 6. Image Segmentation - p. 303 12. Chapter 7. Model Visualization - p. 381 13. Chapter 8. Model Compression - p. 419 14. 8.1 Lightweight Model Design - p. 419 15. 8.2 Model Pruning - p. 436 16. 8.3 Model Quantization - p. 448 17. 8.4 Model Distillation - p. 464 18. 8.5 Structured Model Pruning Practice - p. 470 19. 8.6 8-Bit Model Quantization Practice - p. 479 20. 8.7 Classic Distillation Method Practice - p. 488 21. 8.8 Summary - p. 493 22. Chapter 9. Model Deployment and Launch - p. 497 23. Index - p. 521
Deep Learning for Image Recognition provides a detailed explanation of the fundamental theories underpinning image recognition and code for recognition tasks in specific application scenarios. Readers can manipulate the existing code, thereby deepening their understanding. Chapters include project work enabling readers to apply the skills and knowledge gained from that section of the book. Projects are based on the accessible Pytorch framework, which is straightforward to learn and can be replicated and modified. Readers are presented with current research findings and up to date techniques in image recognition and deep learning.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Chapter 1. Fundamentals of Neural Networks and Convolutional Neural Networks - p. 1 7. Chapter 2. Fundamentals of Deep Learning Optimization Techniques - p. 33 8. Chapter 3. Data Usage in Deep Learning - p. 89 9. Chapter 4. Image Classification - p. 157 10. Chapter 5. Object Detection - p. 221 11. Chapter 6. Image Segmentation - p. 303 12. Chapter 7. Model Visualization - p. 381 13. Chapter 8. Model Compression - p. 419 14. 8.1 Lightweight Model Design - p. 419 15. 8.2 Model Pruning - p. 436 16. 8.3 Model Quantization - p. 448 17. 8.4 Model Distillation - p. 464 18. 8.5 Structured Model Pruning Practice - p. 470 19. 8.6 8-Bit Model Quantization Practice - p. 479 20. 8.7 Classic Distillation Method Practice - p. 488 21. 8.8 Summary - p. 493 22. Chapter 9. Model Deployment and Launch - p. 497 23. Index - p. 521
