Backpropagation_implementation

This repo. implements the backpropagtion algorithm widely used in Machine Learning. The user inputs the number of hidden neurons while the algorithm trains the Neural Network on the created model and the training dataset. Finally, the the machine predicts the outcomes on the testing dataset and plots the associated loss.

To implement, requisites are:

1. random
2. numpy
3. math
4. matplotlib (for plotting loss)

Step 1:

Download/clone the repo.

Step 2:

Add the correct file names for the training and test datasets that are given (or you could use your own 1 dimensional datasets)

Step 3:

Run the program using python3 backprop2.py