LSTM

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to model sequential data by learning long-term dependencies, overcoming the vanishing gradient problem of traditional RNNs. It uses specialized memory cells with gates (input, forget, output) to control information flow, making it effective for tasks like time series forecasting, natural language processing, and speech recognition.

Also known as: Long Short-Term Memory, LSTM Network, LSTM RNN, LSTMs, Long Short Term Memory

Why learn LSTM?

Developers should learn LSTM when working with sequential or time-dependent data where context over long sequences is crucial, such as in language translation, sentiment analysis, or stock price prediction. It is particularly useful in deep learning applications where traditional RNNs fail to capture long-range patterns, offering improved accuracy in models for text, audio, and sensor data.

Learning Resources

• tutorial: Understanding LSTM Networks
• docs: LSTM in TensorFlow Documentation
• video: LSTM Explained | Deep Learning Tutorial
• book: Deep Learning Book - Chapter on RNNs and LSTMs