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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The official code repository for the second edition of the O'Reilly book Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play.
| Date | Stars |
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| 2026-07-24 | 1531 |
| 2026-07-25 | 1532 |
| 2026-07-28 | 1532 |
| 2026-07-30 | 1532 |
| 2026-07-31 | 1534 |
| 2026-08-06 | 1537 |
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# 🦜 Generative Deep Learning - 2nd Edition Codebase The official code repository for the second edition of the O'Reilly book *Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play*. [O'Reilly link](https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/) [Amazon US link](https://www.amazon.com/Generative-Deep-Learning-Teaching-Machines/dp/1098134184/) <img src="img/book_cover.png" width="300px"> ## 📖 Book Chapters 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 ## 🌟 Star History <img src="https://api.star-history.com/svg?repos=davidADSP/Generative_Deep_Learning_2nd_Edition&type=Date" width="500px"> ## 🚀 Getting Started ### Kaggle API 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](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. ### The .env file 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> ``` ### Get set up with Docker This codebase is designed to be run with [Docker](https://docs.docker.com/get-docker/). If you've never used Docker before, don't worry! I have included a guide to Docker in the [Docker README](./docs/docker.md) 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. ### Building the Docker image 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 ``` ### Running the container 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. ## 🏞️ Downloading data The codebase comes with an in-built data downloader helper script. Run the data downloader as follows (from outside the container), choosing one of the named datasets below: ``` bash scripts/download.sh [faces, bricks, recipes, flowers, wines, cellosuites, chorales] ``` ## 📈 Tensorboard 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 ``` ## ☁️ Using a cloud virtual machine To set up a virtual machine with GPU in Google Cloud Platform, follow the instructions in the [Google Cloud README](./docs/googlecloud.md) file in this repository. ## 📦 Other resources Some of the examples in this book are adapted from the excellent open source implementations that
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:97da64e10e2a698f, topic:deep-learning, topic:tensorflow
matched fp:97da64e10e2a698f, topic:stable-diffusion, topic:diffusion-models
matched fp:97da64e10e2a698f, topic:chatgpt