Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models, and world models.

Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative.

• Discover how variational autoencoders can change facial expressions in photos
• Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation
• Create recurrent generative models for text generation and learn how to improve the models using attention
• Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting
• Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

• Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
• Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
• Programming PyTorch for Deep Learning: Creating and Deploying Deep Learning Applications
• Programming Quantum Computers: Essential Algorithms and Code Samples
• Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Practical Time Series Analysis: Prediction with Statistics and Machine Learning
• Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
• Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data
• Deep Learning from Scratch: Building with Python from First Principles