Preface

• Who this book is for
• What this book covers
• To get the most out of this book
• Get in touch

1. Section 1: Fundamentals of Deep Learning for Computer Vision

2. Artificial Neural Network Fundamentals FREE CHAPTER

• Comparing AI and traditional machine learning
• Learning about the ANN building blocks
• Implementing feedforward propagation
• Implementing backpropagation
• Understanding the impact of the learning rate
• Summarizing the training process of a neural network
• Summary
• Questions

3. PyTorch Fundamentals

• Installing PyTorch
• PyTorch tensors
• Building a neural network using PyTorch
• Using a sequential method to build a neural network
• Saving and loading a PyTorch model
• Summary
• Questions

4. Building a Deep Neural Network with PyTorch

• Representing an image
• Why leverage neural networks for image analysis?
• Preparing our data for image classification
• Training a neural network
• Scaling a dataset to improve model accuracy
• Understanding the impact of varying the batch size
• Understanding the impact of varying the loss optimizer
• Building a deeper neural network
• Understanding the impact of batch normalization
• The concept of overfitting
• Summary
• Questions

5. Section 2: Object Classification and Detection

6. Introducing Convolutional Neural Networks

• The problem with traditional deep neural networks
• Building blocks of a CNN
• Implementing a CNN
• Classifying images using deep CNNs
• Visualizing the outcome of feature learning
• Building a CNN for classifying real-world images
• Summary
• Questions

7. Transfer Learning for Image Classification

• Introducing transfer learning
• Understanding the VGG16 architecture
• Understanding the ResNet architecture
• Implementing facial keypoint detection
• Implementing age estimation and gender classification
• Introducing the torch_snippets library
• Summary
• Questions

8. Practical Aspects of Image Classification

• Generating CAMs
• Understanding the impact of data augmentation and batch normalization
• Practical aspects to take care of during model implementation
• Summary
• Questions

9. Basics of Object Detection

• Introducing object detection
• Creating a bounding-box ground truth for training
• Understanding region proposals
• Understanding IoU
• Non-max suppression
• Mean average precision
• Training R-CNN-based custom object detectors
• Training Fast R-CNN-based custom object detectors
• Summary
• Questions

10. Advanced Object Detection

• Components of modern object detection algorithms
• Training Faster R-CNN on a custom dataset
• Working details of YOLO
• Training YOLO on a custom dataset
• Working details of SSD
• Training SSD on a custom dataset
• Summary
• Questions

11. Image Segmentation

• Exploring the U-Net architecture
• Performing upscaling
• Implementing semantic segmentation using U-Net
• Exploring the Mask R-CNN architecture
• Implementing instance segmentation using Mask R-CNN
• Predicting multiple instances of multiple classes
• Summary
• Questions

12. Applications of Object Detection and Segmentation

• Multi-object instance segmentation
• Human pose detection
• Crowd counting
• Image colorization
• 3D object detection with point clouds
• Action recognition from video
• Summary
• Questions

13. Section 3: Image Manipulation

14. Autoencoders and Image Manipulation

• Understanding autoencoders
• Understanding variational autoencoders
• Performing an adversarial attack on images
• Understanding neural style transfer
• Understanding deepfakes
• Summary
• Questions

15. Image Generation Using GANs

• Introducing GANs
• Using GANs to generate handwritten digits
• Using DCGANs to generate face images
• Implementing conditional GANs
• Summary
• Questions

16. Advanced GANs to Manipulate Images

• Leveraging the Pix2Pix GAN
• Leveraging CycleGAN
• Leveraging StyleGAN on custom images
• Introducing SRGAN
• Summary
• Questions

17. Section 4: Combining Computer Vision with Other Techniques

18. Combining Computer Vision and Reinforcement Learning

• Learning the basics of reinforcement learning
• Implementing Q-learning
• Implementing deep Q-learning
• Implementing deep Q-learning with the fixed targets model
• Implementing an agent to perform autonomous driving
• Summary
• Questions

19. Combining Computer Vision and NLP Techniques

• Introducing transformers
• Implementing ViTs
• Transcribing handwritten images
• Document layout analysis
• Visual question answering
• Summary
• Questions

20. Foundation Models in Computer Vision

• Introducing CLIP
• Introducing SAM
• Introducing diffusion models
• Understanding Stable Diffusion
• Summary
• Questions

21. Applications of Stable Diffusion

• In-painting
• ControlNet
• SDXL Turbo
• DepthNet
• Text to video
• Summary
• Questions

22. Moving a Model to Production

• Understanding the basics of an API
• Creating an API and making predictions on a local server
• Containerizing the application
• Shipping and running the Docker container on the cloud
• Identifying data drift
• Using vector stores
• Summary
• Questions

23. Other Books You May Enjoy

24. Index

Appendix