Introduction to neural networks

This repository includes a series of python codes to introduce the user to the main concepts about Neural Networks. These examples are part of my webinar/presentation about this topic. The presentation can be found here Introduction to neural networks.

Code:

• neuron.py includes the code to create a basic neuron that uses different activation functions.

• basic-neuron.py contains the code to create a basic neuron that is able to simulate logic functions: AND and OR by using step function.

• basic_two_layer_neural_network.py contains the code to create a basic neural network with:

  • two inputs: x1, and x2
  • a hidden layer with two neurons: h1 and h2
  • an output layer with a neuron: o1

• two_layer_neural_network.py contains the code to create a neural network with:

  • input_layer_size inputs
  • a hidden layer with hidden_layer_size neurons
  • an output layer with one neuron

• train-two-layer-neural-network.py contains the code to create and train a basic neural network with:

  • two inputs: x1, and x2
  • a hidden layer with two neurons: h1 and h2
  • an output layer with a neuron: o1

Setup

The neural networks introduced here are implemented from scratch and using basic operations. There is no need for any kind of specific python packages, but numpy and matplotlib.

An easy way to install this dependency is using the environment file included in ./auxfiles/ folder, ./auxfiles/nnenv.yml by using conda.

conda env create -f ./auxfiles/nnenv.yml
source activate nnenv

It could be done by running the following command and using the nnenv environemt (as it follows):

source activate nnenv

Or by installing numpy and matplotlib using pip or conda in your system:

conda

conda install numpy matplotlib

pip

pip install numpy matplotlib