Houses some machine learning and neural network implementations. Due to the restrictions of in-line mathematics in github markdown, I embed latex based pngs for better readability.
Requires train and test directories in each dataset directory for training and inference, respectively.
As a basic example I've implemented a logistic regression scheme using a sigmoid function and the gradient descent method in both numpy and pytorch. The code is maths heavy. Below is a complete derivation of the single sample case. As well as its vectorised extension actually implemented. We vectorise to reduce computing times by leveraging low-level optimised matrix multiplications over explicit top-level for-loops.
Hidden layer using ReLU
We extend the logistic regression case using a hidden layer based on the ReLU function.
