Machine Learning

How to Code a Neural Network with Backpropagation In Python (from scratch)

Last Updated on December 1, 2019

The backpropagation algorithm is used in the classical feed-forward artificial neural network.

It is the technique still used to train large deep learning networks.

In this tutorial, you will discover how to implement the backpropagation algorithm for a neural network from scratch with Python.

After completing this tutorial, you will know:

  • How to forward-propagate an input to calculate an output.
  • How to back-propagate error and train a network.
  • How to apply the backpropagation algorithm to a real-world predictive modeling 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 Nov/2016: Fixed a bug in the activate() function. Thanks Alex!
  • Update Jan/2017: Fixes issues with Python 3.
  • Update Jan/2017: Updated small bug in update_weights(). Thanks Tomasz!
  • Update Apr/2018: Added direct link to CSV dataset.
  • Update Aug/2018: Tested and updated to work with Python 3.6.
  • Update Sep/2019: Updated wheat-seeds.csv to fix formatting issues.
How to Implement the Backpropagation Algorithm From Scratch In PythonTo finish reading, please visit source site