What is LSTM

LSTM or Long Short-Term Memory is a type of neural network architecture used in machine learning to overcome the limitations of traditional Recurrent Neural Networks (RNNs) when it comes to handling long-term dependencies and memory.

Recurrent Neural Networks, or RNNs, are a class of neural networks that are primarily designed for sequential data where there is a dependence between past and current inputs. However, traditional RNNs suffer from a vanishing gradient problem, which can cause the network to forget important information over time, resulting in poor performance in tasks that require long-term memory such as natural language processing or speech recognition.

LSTM networks were introduced to solve this vanishing gradient problem by using a memory cell that allows information to be stored over a long period of time. This memory cell has three gates: an input gate, a forget gate, and an output gate.

The input gate regulates the flow of information into the memory cell, the forget gate determines which information to delete from the cell, and the output gate controls the release of information from the cell.

The memory cell is used to store the long-term dependencies between the previous and current inputs, while the output of the LSTM network is used as the input for the next iteration.

LSTM networks have shown remarkable performance in a variety of applications where the processing of sequential data is required. For example, in natural language processing, LSTM networks can be used to predict the next word in a sentence, translate one language to another, and even generate new text.

In conclusion, LSTM is an important neural network architecture for handling sequential data that suffer from vanishing gradient problems. Its ability to maintain long-term dependencies and memory has made it an essential tool in various fields such as natural language processing, speech recognition, and more.