An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the […]

# Archive | Long Short-Term Memory Networks

## A Gentle Introduction to Exploding Gradients in Neural Networks

Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model […]

## What is Teacher Forcing for Recurrent Neural Networks?

Teacher forcing is a method for quickly and efficiently training recurrent neural network models that use the ground truth from […]

## How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras

The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine […]

## Gentle Introduction to Global Attention for Encoder-Decoder Recurrent Neural Networks

The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems such as machine […]

## Difference Between Return Sequences and Return States for LSTMs in Keras

The Keras deep learning library provides an implementation of the Long Short-Term Memory, or LSTM, recurrent neural network. As part […]

## Implementation Patterns for the Encoder-Decoder RNN Architecture with Attention

The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in […]

## How to Develop an Encoder-Decoder Model with Attention in Keras

The encoder-decoder architecture for recurrent neural networks is proving to be powerful on a host of sequence-to-sequence prediction problems in […]

## A Gentle Introduction to RNN Unrolling

Recurrent neural networks are a type of neural network where the outputs from previous time steps are fed as input […]

## Making Predictions with Sequences

Sequence prediction is different from other types of supervised learning problems. The sequence imposes an order on the observations that […]