Introduction
Chapter 1: Building and Training Your First Neural Network
Chapter 2: Capturing Trends and Recognizing Patterns with Dense Neural Networks
Chapter 3: Processing Images with Convolutional Neural Networks
Chapter 4: Enhancing, Generating, and Analyzing Data with Autoencoders
Chapter 5: Segmenting and Analyzing Images with U-Nets
Chapter 6: Training Neural Networks with Self-Supervised Learning
Chapter 7: Processing Time Series and Language with Recurrent Neural Networks
Chapter 8: Processing Language and Classifying Images with Attention and Transformers
Chapter 9: Creating and Transforming Images with Generative Adversarial Networks
Chapter 10: Implementing Generative AI with Diffusion Models
Chapter 11: Modeling Molecules and Complex Systems with Graph Neural Networks
Chapter 12: Continuously Improving Performance with Active Learning
Chapter 13: Mastering Decision-Making with Deep Reinforcement Learning
Chapter 14: Predicting Chaos with Reservoir Computing
Conclusion
Index
