Deep learning neural networks are very easy to create and evaluate in Python with Keras, but you must follow a strict model life-cycle. In this post you will discover the step-by-step life-cycle for creating, training and evaluating deep learning neural networks in Keras and how to make predictions with a trained model. After reading this […]

# Search results for "Machine Learning"

## Text Generation With LSTM Recurrent Neural Networks in Python with Keras

Recurrent neural networks can also be used as generative models. This means that in addition to being used for predictive models (making predictions) they can learn the sequences of a problem and then generate entirely new plausible sequences for the problem domain. Generative models like this are useful not only to study how well a […]

## Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras

A powerful and popular recurrent neural network is the long short-term model network or LSTM. It is widely used because the architecture overcomes the vanishing and exposing gradient problem that plagues all recurrent neural networks, allowing very large and very deep networks to be created. Like other recurrent neural networks, LSTM networks maintain state, and […]

## Sequence Classification with LSTM Recurrent Neural Networks in Python with Keras

Sequence classification is a predictive modeling problem where you have some sequence of inputs over space or time and the task is to predict a category for the sequence. What makes this problem difficult is that the sequences can vary in length, be comprised of a very large vocabulary of input symbols and may require […]

## How To Build Multi-Layer Perceptron Neural Network Models with Keras

The Keras Python library for deep learning focuses on the creation of models as a sequence of layers. In this post you will discover the simple components that you can use to create neural networks and simple deep learning models using Keras. Let’s get started. Update Mar/2017: Updated example for Keras 2.0.2, TensorFlow 1.0.1 and […]

## Assessing and Comparing Classifier Performance with ROC Curves

The most commonly reported measure of classifier performance is accuracy: the percent of correct classifications obtained. This metric has the advantage of being easy to understand and makes comparison of the performance of different classifiers trivial, but it ignores many of the factors which should be taken into account when honestly assessing the performance of […]

## Non-Linear Classification in R with Decision Trees

In this post you will discover 7 recipes for non-linear classification with decision trees in R. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species. Let’s get […]

## Non-Linear Classification in R

In this post you will discover 8 recipes for non-linear classification in R. Each recipe is ready for you to copy and paste and modify for your own problem. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of […]

## Linear Classification in R

In this post you will discover recipes for 3 linear classification algorithms in R. All recipes in this post use the iris flowers dataset provided with R in the datasets package. The dataset describes the measurements if iris flowers and requires classification of each observation to one of three flower species. Let’s get started. Logistic […]

## Non-Linear Regression in R with Decision Trees

In this post, you will discover 8 recipes for non-linear regression with decision trees in R. Each example in this post uses the longley dataset provided in the datasets package that comes with R. The longley dataset describes 7 economic variables observed from 1947 to 1962 used to predict the number of people employed yearly. Let’s get started. Classification […]