The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.

https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1098134184/

Below is a outline of the book chapters:

Part I: Introduction to Generative Deep Learning

1. Generative Modeling
2. Deep Learning

Part II: Methods

3. Variational Autoencoders
4. Generative Adversarial Networks
5. Autoregressive Models
6. Normalizing Flows
7. Energy-Based Models
8. Diffusion Models

Part III: Applications

9. Transformers
10. Advanced GANs
11. Music Generation
12. World Models
13. Multimodal Models
14. Conclusion

Many of the examples in this book are adapted from the excellent open source implementations that are available through the Keras website (https://keras.io/examples/generative/). I highly recommend you check out this resource as new models and examples are constantly being added.

To download some of the datasets for the book, you will need a Kaggle account and an API token. To use the Kaggle API:

1. Sign up for a Kaggle account at https://www.kaggle.com.
2. Go to the 'Account' tab of your user profile
3. Select 'Create API Token'. This will trigger the download of kaggle.json, a file containing your API credentials.

Create a file called .env in the root directory, containing the following values (replacing the Kaggle username and API key with the values from the JSON):

JUPYTER_PORT=8888
TENSORBOARD_PORT=6006
KAGGLE_USERNAME=<your_kaggle_username>
KAGGLE_KEY=<your_kaggle_key>

This codebase is designed to be run with Docker.

If you've never used Docker before, don't worry! I have included a guide to Docker in the Docker README file in this repository. This includes a full run through of why Docker is awesome and a brief guide to the Dockerfile and docker-compose.yml for this project.

If you do not have a GPU, run the following command:

docker-compose build

If you do have a GPU that you wish to use, run the following command:

docker-compose -f docker-compose-gpu.yml build

If you do not have a GPU, run the following command:

docker-compose up

If you do have a GPU that you wish to use, run the following command:

docker-compose -f docker-compose-gpu.yml up

Jupyter will be available in your local browser, on the port specified in your env file - for example

http://localhost:8888

The notebooks that accompany the book are available in the /notebooks folder, organized by chapter and example.

The codebase comes with an in-built data downloader helper script. Use the helper script as follows, choosing one of the named datasets below (from outside the container):

bash scripts/download.sh [faces, bricks, recipes, flowers, wines, cellosuites, chorales]

Tensorboard is really useful for monitoring experiments and seeing how your model training is progressing.

To launch Tensorboard, run the following script (from outside the container):

• <CHAPTER> - the required chapter (e.g. 03_vae)
• <EXAMPLE> - the required example (e.g. 02_vae_fashion)

bash scripts/tensorboard.sh <CHAPTER> <EXAMPLE>

Tensorboard will be available in your local browser on the port specified in your .env file - for example:

http://localhost:6006

To set up a virtual machine with GPU in Google Cloud Platform, follow the instructions in the Google Cloud README file in this repository.