When I’m learning a new topic, I used to take notes. The very act of writing helps me concentrate better, and also remember the information for a longer time. Ultimately, I found writing to be slow and tiring, and so I switched to typing such notes in org-mode. (See my other post for why I chose org-mode). I take a similar approach with Machine Learning concepts. I start with notes on theory, and then move to apply those concepts in projects.
Recently, I was interested in Attention mechanisms and came across this impressive project: greentfrapp/attention-primer. The project starts with a simple implementation of the Scaled Dot-Product Attention mechanism, demonstrated on a toy example. It then proceeds to add and demonstrate the other basic units of the Transformer architecture. The code and the theory is nicely explained, and I highly encourage others to go through the 4 Tasks as well. Thanks @greentfrapp!
The project uses TensorFlow (<1.0). To support my learning, I converted the TensorFlow code to PyTorch. This way, I’m more aware of the minute details of the implementation.
The fork with PyTorch code is here: sainathadapa/attention-primer-pytorch.