Knowledge Labs
Logo
Introduction to Machine LearningDeep Learning IntroductionProbability for Machine LearningStatistics for Machine LearningNatural Language Processing (NLP)Data Science IntroductionAI for EveryoneRetrieval Augmented Generation (RAG)AI for ArtistsComprehensive Guide to Model Context ProtocolComprehensive Guide to Economy & Financial SystemsComprehensive Guide to Babylon.js Game Engine
Deep Learning Introduction
/Neural Network Architectures
/Recurrent Neural Networks (RNNs)
/Long Short-Term Memory (LSTM)

Long Short-Term Memory (LSTM)

.

Long Short-Term Memory (LSTM)

Long Short-Term Memory networks represent one of the most important architectural innovations in deep learning history. Developed to solve the vanishing gradient problem that plagued standard RNNs, LSTMs use an ingenious system of gates and memory cells that allow information to flow unchanged for long periods.

Think of an LSTM as a sophisticated note-taking system with three key components: a forget gate that decides which information to discard, an input gate that determines which new information to store, and an output gate that controls what information to pass along. This gating mechanism allows the network to selectively remember or forget information over long sequences.

This breakthrough architecture enabled machines to maintain context over hundreds of timesteps, making possible applications like handwriting recognition, speech recognition, machine translation, and music composition. Before transformers dominated natural language processing, LSTMs were the workhorse behind most language technologies, and they remain vital for time-series forecasting where their ability to capture long-term dependencies and temporal patterns is invaluable.

The impact of LSTMs extends beyond their direct applications—their success demonstrated that carefully designed architectural innovations could overcome fundamental limitations in neural networks, inspiring further research into specialized architectures.