Generative Deep Learning - 2nd Edition Codebase
The Generative Deep Learning project is the official repository supporting the second edition of the O'Reilly book titled Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play. This book and its accompanying codebase are aimed at exploring the fascinating field of generative models, which enable machines to create new content in various forms such as images, text, music, and more.
Book Chapters Overview
The book is structured into three parts, guiding readers through the fundamental concepts and advanced applications of generative deep learning:
Part I: Introduction to Generative Deep Learning
- Generative Modeling: An overview of generative models and their capabilities.
- Deep Learning: A refresher on core deep learning concepts that are essential for understanding generative models.
Part II: Methods
- Variational Autoencoders (VAEs): Models that learn efficient coding schemes for data generation.
- Generative Adversarial Networks (GANs): A framework involving two neural networks contesting each other to produce more realistic data.
- Autoregressive Models: Models that predict future data points based on past sequences.
- Normalizing Flows: Techniques that enable exact likelihood computation in complex generative scenarios.
- Energy-Based Models: Models that define probability distributions in terms of energy.
- Diffusion Models: Models inspired by physical diffusion processes applied to image creation.
Part III: Applications
- Transformers: Popular architecture used widely in sequence tasks such as language modeling.
- Advanced GANs: Exploration of GAN variations and improvements.
- Music Generation: Techniques for generating music using machine learning.
- World Models: Where AI simulates understanding of complex environments.
- Multimodal Models: Models optimizing performance across various data types.
- Conclusion: A wrap-up of the discussed topics and future directions.
Getting Started with the Codebase
To explore the book's practical examples, users are encouraged to engage with tools like Docker, Kaggle API, and Tensorboard, which facilitate setup and experimentation.
-
Kaggle API: Obtain datasets essential for implementations by creating an account and setting up API credentials.
-
Docker: A platform to easily build, handle, and run applications. The book provides a simple guide for setting up Docker to run the project's code efficiently, with or without GPU support.
-
Tensorboard: Valuable for visualizing model training progress; instructions are provided on how to run it for different chapters and examples.
Data and Cloud Resources
The project includes scripts for downloading datasets used in examples, enabling seamless data acquisition for hands-on learning. Additionally, for those needing more computational power, guidelines are offered for setting up virtual machines with GPU support on Google Cloud Platform.
Utilizing Other Resources
Many book examples draw from open-source implementations available through the Keras website, highlighting the project’s connection with broader community resources and encouraging exploration of additional models beyond what the book covers.
This codebase not only complements the knowledge shared in the book but also serves as a practical playground for enthusiasts and professionals alike, to delve deeper into the capabilities of generative artificial intelligence.