Introduction to neural networks

Artificial neural networks (ANN) or, basically, neural networks (NN) are computing systems inspired by the biological neural networks that constitute animal brains. They have become one of the main models applied in machine learning and deep learning to address the most challenging tasks.

In this post, I am sharing a presentation that is part of my seminar about Generative Adversarial Networks. This presentation contains a set of different examples in python of neural networks developed from their basis. The idea is to allow the learner to understand the main foundations and operation of neural networks and back-propagation.

• Slides
• GitHub Code:

GitHub 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