Sequence prediction is a problem that involves using historical sequence information to predict the next value or values in the […]

# Archive | Long Short-Term Memory Networks

## How to One Hot Encode Sequence Data in Python

Machine learning algorithms cannot work with categorical data directly. Categorical data must be converted to numbers. This applies when you […]

## A Tour of Recurrent Neural Network Algorithms for Deep Learning

Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to […]

## Attention in Long Short-Term Memory Recurrent Neural Networks

The Encoder-Decoder architecture is popular because it has demonstrated state-of-the-art results across a range of domains. A limitation of the […]

## How to Prepare Sequence Prediction for Truncated BPTT in Keras

Recurrent neural networks are able to learn the temporal dependence across multiple timesteps in sequence prediction problems. Modern recurrent neural […]

## Techniques to Handle Very Long Sequences with LSTMs

Long Short-Term Memory or LSTM recurrent neural networks are capable of learning and remembering over long sequences of inputs. LSTMs […]

## A Gentle Introduction to Backpropagation Through Time

Backpropagation Through Time, or BPTT, is the training algorithm used to update weights in recurrent neural networks like LSTMs. To […]

## Data Preparation for Variable Length Input Sequences

Deep learning libraries assume a vectorized representation of your data. In the case of variable length sequence prediction problems, this […]

## How to Develop a Bidirectional LSTM For Sequence Classification in Python with Keras

Bidirectional LSTMs are an extension of traditional LSTMs that can improve model performance on sequence classification problems. In problems where […]

## How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers

A powerful feature of Long Short-Term Memory (LSTM) recurrent neural networks is that they can remember observations over long sequence […]