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Full Description
Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.
Contents
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
