Deep Learning book the covers the principles of deep learning, motivation, explanations, state of the art papers for the various tasks and architectures:
- Data Preprocessing
- Weight Initialization
- Activatation Functions
- Loss functions
- Optimization
- Regularization
- Convolutional Neural Netowrks
- Object detection
- Semantic Segmentation
- Generative models
- Denoising
- Super resolution
- Style transfer and style manipulation
- Inpaintig
- Self supervised learning
- Vision Transformers
- OCR
- Multi modal