This small project is for my education purpose, which I try to implement in Python the main concepts of a vanilla Multilayer Perceptron.

The concepts worked here were:

• Feedfoward process
• Backpropagation
• Gradient Checking
• Create distinct neural net structures
• Use distinct batch sizes

The scripts allow:

• To work with two problems: linear-regression and logistic-regression;
• To instance a neural net with any number of hidden layers and any number of units;
• To use or not the bias elemnt in the layer;
• To use gradient checking;
• To set any size of batch;

The restrictions are:

• Only work with an output of one dimension;
• All activation functions are softmax function;

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To see an example of how to train a neural net, check the main notebook.