Machine Learning

Regression Tutorial with the Keras Deep Learning Library in Python

Last Updated on August 27, 2020

Keras is a deep learning library that wraps the efficient numerical libraries Theano and TensorFlow.

In this post you will discover how to develop and evaluate neural network models using Keras for a regression problem.

After completing this step-by-step tutorial, you will know:

  • How to load a CSV dataset and make it available to Keras.
  • How to create a neural network model with Keras for a regression problem.
  • How to use scikit-learn with Keras to evaluate models using cross-validation.
  • How to perform data preparation in order to improve skill with Keras models.
  • How to tune the network topology of models with Keras.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
  • Update Mar/2018: Added alternate link to download the dataset as the original appears to have been taken down.
  • Update Apr/2018: Changed nb_epoch argument to epochs.
  • Update Sep/2019: Updated for Keras 2.2.5 API.
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