This project is part of the Udacity's Deep Learning Nanodegree.
The purpose of this project is to build a simple Neural Network from scratch to predict problem on real dataset from UCI Machine Learning Database The network has two layers, a hidden and an output layer. For the hidden layer, the sigmoid function is used for activation. The ouput layer uses regression, thus the output is the same as the input. The main techniques used in this project are linear regression, gradient descent, back and forward propagation.
#Dependencies
- matplotlib
- numpy
- pandas
For convenience, I include "environment.yaml" and "environment.txt" for installation using conda or pip
#Usage
Type jupyter notebook Neural-Network.ipynb to the terminal.