Long Short-Term Memory

An LSTM is a type of artificial neural network that's really good at processing sequences of data, like spoken language or text. What makes it special is its ability to handle "long-term dependencies," meaning it can connect information it learned much earlier in a sequence to what it's learning now. Traditional recurrent neural networks sometimes struggle with this, forgetting older information as new data comes in.

Invented in 1997 by Sepp Hochreiter and Jürgen Schmidhuber, LSTMs achieve this through a unique internal structure called "gates" (input, forget, and output gates) which regulate the flow of information into and out of the network's memory cell. These gates essentially decide what information is important enough to keep, what to throw away, and what to output. This mechanism allows LSTMs to selectively remember or forget parts of their memory, making them highly effective for tasks like speech recognition, machine translation, and even predicting stock prices where patterns unfold over time.

While newer architectures like the Transformer have become dominant in many areas, particularly for very large language models, LSTMs were a significant breakthrough and are still used in many applications where their strengths in sequence processing are valuable. They laid much of the groundwork for understanding how networks can manage information over extended sequences, influencing later developments in deep learning.