Dropout is a simple and powerful regularization technique for neural networks and deep learning models. 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 […]
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 to checkpoint your deep learning models during training in Python using the Keras library. Let’s get started. Jun/2016: First published Update Mar/2017: Updated […]
Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure, and a lot of parameters need to be set. On top of that, individual models can be very slow to train. In this post, you will discover how to use the grid search capability from […]
Keras is an easy-to-use and powerful Python library for deep learning. There are a lot of decisions to make when designing and configuring your deep learning models. Most of these decisions must be resolved empirically through trial and error and by evaluating them on real data. As such, it is critically important to have a […]
Machine learning is a broad topic. Deep learning, in particular, is a way of using neural networks for machine learning. A neural network is probably a concept older than machine learning, dating back to the 1950s. Unsurprisingly, there were many libraries created for it. The following aims to give an overview of some of the […]
Keras is a simple and powerful Python library for deep learning. Since deep learning models can take hours, days, and even weeks to train, it is important to know how to save and load them from a disk. In this post, you will discover how to save your Keras models to files and load them […]
Keras is a powerful and easy-to-use free open source Python library for developing and evaluating deep learning models. It is part of the TensorFlow library and allows you to define and train neural network models in just a few lines of code. In this tutorial, you will discover how to create your first deep learning neural […]
You’ve probably been told to standardize or normalize inputs to your model to improve performance. But what is normalization and how can we implement it easily in our deep learning models to improve performance? Normalizing our inputs aims to create a set of features that are on the same scale as each other, which we’ll […]
TensorFlow is a Python library for fast numerical computing created and released by Google. It is a foundation library that can be used to create Deep Learning models directly or by using wrapper libraries that simplify the process built on top of TensorFlow. In this post, you will discover the TensorFlow library for Deep Learning. […]
Prediction intervals provide a measure of uncertainty for predictions on regression problems. For example, a 95% prediction interval indicates that 95 out of 100 times, the true value will fall between the lower and upper values of the range. This is different from a simple point prediction that might represent the center of the uncertainty […]
