It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness. This process can be used to develop effective neural network models for classification and regression predictive modeling problems. In this tutorial, you will […]

It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness. This process can be used to develop effective neural network models for classification and regression predictive modeling problems. In this tutorial, you will […]

Some prediction problems require predicting both numeric values and a class label for the same input. A simple approach is to develop both regression and classification predictive models on the same data and use the models sequentially. An alternative and often more effective approach is to develop a single neural network model that can predict both a numeric and class label value from the same input. This is called a multi-output model and can be relatively easy to develop and […]

It can be challenging to develop a neural network predictive model for a new dataset. One approach is to first inspect the dataset and develop ideas for what models might work, then explore the learning dynamics of simple models on the dataset, then finally develop and tune a model for the dataset with a robust test harness. This process can be used to develop effective neural network models for classification and regression predictive modeling problems. In this tutorial, you will […]

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 famous libraries for neural networks and deep learning. After finishing this tutorial, you will learn: Some of the deep learning or neural network libraries The […]

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, works. After finishing this tutorial, you will learn: What is autograd in TensorFlow How to make use of autograd and an optimizer to solve an […]

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 model architectures, and there’s also the Model subclass which helps reusability. This article will explore the different ways to create models in Keras, along with […]

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. 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. Jun/2016: First published Update Mar/2017: Updated for Keras […]

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, you will explore three popular ones: sigmoid, tanh, and ReLU. After reading this article, you will learn: Why nonlinearity is important in a neural network […]