Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to learn long-term dependencies in sequential data.
LSTMs effectively address the vanishing gradient problem, enabling them to retain information over extended sequences, crucial for tasks like language translation and speech recognition.
An LSTM consists of memory cells and three gates: – Input – Forget – Output gates
Memory cells store information across time steps. They help LSTMs remember important data while discarding irrelevant information.
– Natural Language Processing – Time Series Forecasting – Video Analysis
Bidirectional LSTMs process input sequences in both forward and backward directions, capturing longer-range dependencies and improving prediction accuracy in complex tasks.
LSTMs are vital for modern AI applications, allowing models to learn from past data effectively and making them indispensable.