A Gentle Introduction to LSTM Autoencoders

An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. In this post, you will discover the LSTM Autoencoder model and how to implement it in Python using Keras. A Gentle Introduction to LSTM Autoencoders Photo by Ken Lund, some rights reserved. An autoencoder is a neural network model that seeks to learn a compressed representation of an input.