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 […]

Last Updated on August 27, 2020 Deep learning models can take hours, days or even weeks to train. If the run is stopped unexpectedly, you can lose a lot of work. In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. 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 […]

Last Updated on October 3, 2019 You can learn a lot about neural networks and deep learning models by observing their performance over time during training. Keras is a powerful library in Python that provides a clean interface for creating deep learning models and wraps the more technical TensorFlow and Theano backends. In this post you will discover how you can review and visualize the performance of deep learning models over time during training in Python with Keras. Kick-start your […]

Last Updated on August 27, 2020 A simple and powerful regularization technique for neural networks and deep learning models is dropout. In this post you will discover the dropout regularization technique and how to apply it to your models in Python with Keras. After reading this post you will know: How the dropout regularization technique works. How to use dropout on your input layers. How to use dropout on your hidden layers. How to tune the dropout level on your […]

Last Updated on August 27, 2020 Training a neural network or large deep learning model is a difficult optimization task. The classical algorithm to train neural networks is called stochastic gradient descent. It has been well established that you can achieve increased performance and faster training on some problems by using a learning rate that changes during training. In this post you will discover how you can use different learning rate schedules for your neural network models in Python using […]