Artificial Neural Network (ANN) Fundamentals

This is the repository for D-Lab’s Artificial Neural Network (ANN) Fundamentals in Python workshop. These materials were authored and contributed by Qingkai Kong based on several of his excellent blog posts and subsequently editted by Sean Perez:

Machine learning 1 - What is machine learning and real world example
Machine learning 2 - Types of Machine Learning
Machine learning 3 - Artificial Neural Networks - part 1- Basics
Machine learning 4 - Artificial Neural Networks - part 2- Step by step implement Perceptron
Machine learning 5 - Artificial Neural Networks - part 3- Step by step implement Muti-Layer Perceptron
Machine learning 6 - Artificial Neural Networks - part 4- sklearn MLP classification example
Machine learning 9 - More on Artificial Neural Network

Content outline:

• Gentle introduction
  • What is machine learning?
  • History of artificial neural networks (ANN)
  • Overview of ANNs - how they work
• Step by step instructions for building ANN
  • Perceptron
  • Backpropagation
  • Bias
  • Activation function
  • Calculating error example
• Real world example
  • scikit learn

Installation Requirements:

• python 3
• numpy
• matplotlib
• sklearn
• tensorflow
• jupyterlab