A simple neural network built from scratch (ie. using only numpy, no tensorflow or pytorch) as an exercise to learn machine learning.
I'm a complete ML noob. There are a lot of tutorials on how to build an NN from scratch but I want to figure it out on my own so I'll probably stumble around make dumb mistakes. I watched 3B1B's series on neural networks to get a theoretical understanding of NNs, but other than that, I'm going in blind. That's my goat btw
"What I cannot create, I do not understand" -Richard Feynman
I'll flesh out this section with what design choices I made once I've finished building it lol
- Once the implementation is done, test it on the MNIST hand-written digits dataset and get a relatively high accuracy (anything >80%).
- Explore some fun deep learning techniques (learning rate schedules, early stopping, different gradient descent variations, hyperparameter tuning, different initializtion methods, etc.).