Long Short Term Memory

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to model sequential data by addressing the vanishing gradient problem in traditional RNNs. It uses specialized memory cells with input, forget, and output gates to selectively retain or discard information over long sequences. This makes LSTMs particularly effective for tasks involving time-series analysis, natural language processing, and speech recognition.

Also known as: LSTM, Long Short-Term Memory Networks, LSTM Networks, LSTM RNN, Long Short Term Memory Neural Networks

Why learn Long Short Term Memory?

Developers should learn LSTM when working on projects that require modeling dependencies in sequential data, such as time-series forecasting (e.g., stock prices or weather patterns), text generation, or machine translation. It is especially useful in scenarios where long-range context is critical, as it mitigates issues like gradient vanishing that plague standard RNNs, enabling more stable training and better performance on complex sequences.

Learning Resources

• tutorial: Understanding LSTM Networks
• docs: LSTM in PyTorch Documentation
• course: Sequence Models Course on Coursera
• video: LSTM Explained | A Complete Overview
• book: Deep Learning Book - Chapter 10