Chapter 2: Training Simple Machine Learning Algorithms for Classification

Chapter Outline

• Artificial neurons – a brief glimpse into the early history of machine learning

  • The formal definition of an artificial neuron

  • The perceptron learning rule

• Implementing a perceptron learning algorithm in Python

  • An object-oriented perceptron API

  • Training a perceptron model on the Iris dataset

• Adaptive linear neurons and the convergence of learning

  • Minimizing cost functions with gradient descent

  • Implementing an Adaptive Linear Neuron in Python

  • Improving gradient descent through feature scaling

  • Large scale machine learning and stochastic gradient descent

• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.