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://learning.oreilly.com/library/view/generative-deep-learning/9781492041931/ https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1492041947/ref=sr_1_1

![Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play](assets/book_cover.png "Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play" | width=100)

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

The subfolders in the notebooks folder correspond to the chapters from the book. Within each chapter, there are several examples of models that are again separated into different folders

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

Follow the instructions here:

https://github.com/Kaggle/kaggle-api

Download the JSON file that stores your username and API key.

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>

To get set up with Docker, follow the instructions in the docker.md file in this repository.

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

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

http://localhost:8888

The codebase comes with an in-built data downloader helper script. Use the script as follows:

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

To launch Tensorboard, run the following script, replacing <CHAPTER> with the required chapter (e.g. 03_vae) and <EXAMPLE> with 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