A multilayer perceptron (MLP) is a fully connected neural network, i.e., all the nodes from the current layer are connected to the next layer. A MLP consisting in 3 or more layers: an input layer, an output layer and one or more hidden layers. Note that the activation function for the nodes in all the layers (except the input layer) is a non-linear function.
Example architecture of a MLP Image from [https://github.com/ledell/sldm4-h2o/blob/master/sldm4-deeplearning-h2o.Rmd]
The Jupyter notebook has as goal to show the use the Multilayer-perceptron class mlp.py provided in this repository. The implementation of the MLP has didactic purposes in other words is not optimized, but well commented. It is mostly based on the lectures for weeks 4 and 5 (neural networks) in the the MOOC Machine Learning taught by from Andrew Ng and notes from the chapter 6 (deep forward networks) from the Deep Learning.