How To Implement The Perceptron Algorithm From Scratch In Python
Last Updated on August 13, 2019
The Perceptron algorithm is the simplest type of artificial neural network.
It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks.
In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python.
After completing this tutorial, you will know:
- How to train the network weights for the Perceptron.
- How to make predictions with the Perceptron.
- How to implement the Perceptron algorithm for a real-world classification problem.
Kick-start your project with my new book Machine Learning Algorithms From Scratch, including step-by-step tutorials and the Python source code files for all examples.
Let’s get started.
- Update Jan/2017: Changed the calculation of fold_size in cross_validation_split() to always be an integer. Fixes issues with Python 3.
- Update Aug/2018: Tested and updated to work with Python 3.6.
How To Implement The Perceptron Algorithm From Scratch In Python
Photo by Les Haines, some rights reserved.