TinyDL

• Tiny Deep learning library
• DOCUMENTATION
• Check the demo.py or the demo notebook!!! (same content)
• Have a look at what works so far :)
• Only needs numpy and pycuda for the library

Contributions

• Do check the todo.md for stuff you can work on :)
• Welcome! Add an issue if you have something you want to look at
• PRs welcome

Requirements

• numpy
• matplotlib
• pycuda (for GPU)
• pandas (if you are using the helpers and need to read a table)

How to run

• Install the requirements
• Configure parameters in config.py
• python demo.py

Why?

• Pytorch is too complicated to learn from. (Please. I tried. There are a million folders. My brain >.<)
• This does not intend to be Pytorch. Just to understand what goes into it
• An attempt at recreating most of the essential components from scratch
• Eventual blogs on it as well

Inspired by

• Karpathy and his awesomeness xD
• micrograd
• teddykokker
• ft. Every attempt of me trying this in other languages and failing miserably ):

What are the .sh files

• I am lazy. So I write code when I have to do the same things again and again.

pusher.sh

• Formats all the code using "Black" formatter
• Creates documentation from docstrings in the code using pandoc
• Fixes the documentation paths for working with Github Pages
• Takes the demo.py and pops it into a nice notebook for anyone to run and use
• If an argument is given, it git commits with the message and pushes it to the repository

References

• training and tensor operations
• autograd
• pbar
• skalski
• softmax
• activations1
• albumentations
• conv
• optim
• loss
• loss2
• Countless stackoverflow searches (hehe)