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sequence-models

My personal notes on sequence modelling using deep learning. Contains a mixture of theory in notes and implementations in src and/or notebooks.

  • src: Python modules containing models and helper functions
  • notebooks: Implementations and demos. For more complex architectures, such as the transformer, notebooks can be considered as entry points to the code in src, and so may be quite minimal. See src for implementation details, and notes for theory.
  • notes: My personal notes on sequence models
  • figures: Pictures for notes and notebooks

Installation (CPU only)

This project's environment was set up for the CPU only, as I'm focusing on understanding architecture rather than performance here.

If you're running on Windows, you can try using the environment.yml included with the project. This can be a bit flaky with complicated dependencies, at least in my experience. Otherwise, go to the pytorch installation GUI for the appropriate command, and then once pytorch is installed, then use the environment.yml as a guide for the rest of the dependencies. You can delete problematic dependencies if you encounter errors. Use mamba!

Updating the environment.yml

conda env export --from-history > environment.yml