Deep Learning Crash Course (Paperback)

Introduction
Chapter 1: Dense Neural Networks for Classification
Chapter 2: Dense Neural Networks for Regression
Chapter 3: Convolutional Neural Networks for Image Analysis
Chapter 4: Encoders–Decoders for Latent Space Manipulation
Chapter 5: U-Nets for Image Transformation
Chapter 6: Self-Supervised Learning to Exploit Symmetries
Chapter 7: Recurrent Neural Networks for Timeseries Analysis
Chapter 8: Attention and Transformers for Sequence Processing
Chapter 9: Generative Adversarial Networks for Image Synthesis
Chapter 10: Diffusion Models for Data Representation and Exploration
Chapter 11: Graph Neural Networks for Relational Data Analysis
Chapter 12: Active Learning for Continuous Learning
Chapter 13: Reinforcement Learning for Strategy Optimization
Chapter 14: Reservoir Computing for Predicting Chaos
Conclusion and Outlook