This project contains different models for image classification. Specifically, contains three models:
- A logistic regression model.
- A shallow neural network model.
- A deep neural network with L layers.
The models are built from scratch using numpy. The objective is to gain intuition about how the internals of
neural networks works, with concepts as forward propagation, backward propagation, and gradient descent optimization.
This is a self guide project for learning, while taking some courses about deep learning, and implementing those concepts on my own environment.
The idea is document a binnacle of the process and find out what went wrong after finish it.