Inspired and guided by 3b1b's video on how backpropagation machine learning works. All code, however, is original and my own. Able to acheive 93% testing accuracy with layer sizes of 784,16,16,10; batchsize of 32, learning rate of 0.1, and 10 epochs. Now, I wrote this several months ago and don't remember exactly how long it took to train but definetly something I ran overnight. My code is very inefficient as the main goal of the project was developing my understanding of how basic machine learning works, not making the best code possible :P
Training and Testing Data Used: http://yann.lecun.com/exdb/mnist/
3b1b Video (1/4): https://www.youtube.com/watch?v=aircAruvnKk&ab_channel=3Blue1Brown
Libraries Used: Numpy (Matrix Math), mnist (Importing Training and Testing Data), and Pickle (Storing Trained Weights and Baises for later)