- 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
- 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
- numpy
- matplotlib
- pycuda (for GPU)
- pandas (if you are using the helpers and need to read a table)
- Install the requirements
- Configure parameters in config.py
- python demo.py
- 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
- Karpathy and his awesomeness xD
- micrograd
- teddykokker
- ft. Every attempt of me trying this in other languages and failing miserably ):
- I am lazy. So I write code when I have to do the same things again and again.
- 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
- training and tensor operations
- autograd
- pbar
- skalski
- softmax
- activations1
- albumentations
- conv
- optim
- loss
- loss2
- Countless stackoverflow searches (hehe)