/implementing-MLP-from-scratch

This is a python project to implement the main concepts needed to a vanilla neural net.

Primary LanguageJupyter Notebook

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;

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