🐙 Machine Learning Notebooks

This repo contains machine learning notebooks for different tasks and applications. The notebooks are meant to be minimal, easily reusable, and extendable. You are free to use them for educational and research purposes.

This repo supports Codespaces!

• Spin up a new instance by clicking on the green "<> Code" button followed by the "Configure and create codespace" option. Make sure to select the dev container config provided with this repo. This setups an environment with all the dependencies installed and ready to go.
• Once the codespace is fully running, you can install all the libraries you will need to run the notebooks under the /notebooks folder. Open up a terminal and simply run conda create --name myenv --file spec-file.txt to install all the Python libraries including PyTorch.
• Activate your environment conda activate myenv. You might need to run conda init zsh or whatever shell you are using... and then close + reopen terminal.
• Finally you can try out if everything is working by opening a notebook such as /notebooks/bow.ipynb.

Getting Started

NLP

Transformers

Computer Vision

Generative Adversarial Network

Parameter Efficient Fine-tuning

If you find any bugs or have any questions regarding these notebooks, please open an issue. We will address it as soon as we can.

Reach out on Twitter if you have any questions.

Please cite the following if you use the code examples in your research:

@misc{saravia2022ml,
  title={ML Notebooks},
  author={Saravia, Elvis and Rastogi, Ritvik},
  journal={https://github.com/dair-ai/ML-Notebooks},
  year={2022}
}