Machine Learning

Binary Classification Tutorial with the Keras Deep Learning Library

Last Updated on August 27, 2020

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

Keras allows you to quickly and simply design and train neural network and deep learning models.

In this post you will discover how to effectively use the Keras library in your machine learning project by working through a binary classification project step-by-step.

After completing this tutorial, you will know:

  • How to load training data and make it available to Keras.
  • How to design and train a neural network for tabular data.
  • How to evaluate the performance of a neural network model in Keras on unseen data.
  • How to perform data preparation to improve skill when using neural networks.
  • How to tune the topology and configuration of neural networks in 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 Oct/2016: Updated for Keras 1.1.0 and scikit-learn v0.18.
  • Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
  • Update Sep/2019: Updated for Keras 2.2.5 API.
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