LSTM Full Form – Long Short-Term Memory

LSTM stands for Long Short-Term Memory.

It is a type of recurrent neural network (RNN) architecture that is designed to remember long-term dependencies in sequence data. Unlike traditional RNNs, LSTM networks have a more complex architecture that allows them to learn and remember over long sequences, making them particularly effective for tasks such as natural language processing, time series prediction, and more.

What is LSTM?

Long Short-Term Memory is an improved version of the recurrent neural network designed by Hochreiter & Schmidhuber.

A traditional RNN has a single hidden state that is passed through time, which can make it difficult for the network to learn long-term dependencies. LSTM model addresses this problem by introducing a memory cell, which is a container that can hold information for an extended period.

LSTM architectures are capable of learning long-term dependencies in sequential data, which makes them well-suited for tasks such as language translation, speech recognition, and time series forecasting.

LSTMs can also be used in combination with other neural network architectures, such as Convolutional Neural Networks (CNNs) for image and video analysis.