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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.