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, […]

## Binary Classification Tutorial with the Keras Deep Learning Library

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 networks 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 […]

## Dropout Regularization in Deep Learning Models with Keras

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 […]

## Using Activation Functions in Neural Networks

Activation functions play an integral role in neural networks by introducing nonlinearity. This nonlinearity allows neural networks to develop complex representations and functions based on the inputs that would not be possible with a simple linear regression model. Many different nonlinear activation functions have been proposed throughout the history of neural networks. In this post, […]

## How to Checkpoint Deep Learning Models in Keras

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 […]

## How to Grid Search Hyperparameters for Deep Learning Models in Python with Keras

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 […]

## Three Ways to Build Machine Learning Models in Keras

If you’ve looked at Keras models on Github, you’ve probably noticed that there are some different ways to create models in Keras. There’s the Sequential model, which allows you to define an entire model in a single line, usually with some line breaks for readability. Then, there’s the functional interface that allows for more complicated […]

## Evaluate the Performance of Deep Learning Models in Keras

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 […]

## Using Autograd in TensorFlow to Solve a Regression Problem

We usually use TensorFlow to build a neural network. However, TensorFlow is not limited to this. Behind the scenes, TensorFlow is a tensor library with automatic differentiation capability. Hence you can easily use it to solve a numerical optimization problem with gradient descent. In this post, you will learn how TensorFlow’s automatic differentiation engine, autograd, […]

## Overview of Some Deep Learning Libraries

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 […]