Multi-Layer-Perceptron

Self-Coded Multi-Layer Perceptron without use of available neural network/connectionist/machine learning/... libraries.

This software is completes the following:

• Create a new MLP with any given number of inputs, any number of outputs (can be sigmoidal or linear), and any number of hidden units (sigmoidal/tanh) in a single layer.
• Initialise the weights of the MLP to small random values
• Predict the outputs corresponding to an input vector
• Implement learning by backpropagation

Testing:

1. Train an MLP with 2 inputs, two hidden units and one output on the following examples (XOR function): ((0, 0), 0) ((0, 1), 1) ((1, 0), 1) ((1, 1), 0)
2. At the end of training, check if the MLP predicts correctly all the examples.

In case GitHub can't render the notebook ("Sorry, something went wrong. Reload?"): https://nbviewer.jupyter.org/github/michjord0001/Multi-Layer-Perceptron/blob/master/MultiLayerPerceptron.ipynb