Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) that are capable of learning long-term dependencies. They are particularly useful for tasks that involve sequential data, such as time series prediction, natural language processing, and speech recognition.
Basics of LSTM
Recurrent Neural Networks (RNNs): Traditional RNNs can only handle sequential data by using the hidden state to remember information from previous inputs. However, they suffer from the vanishing gradient problem, which makes it difficult for them to learn long-term dependencies.
LSTM Networks: LSTM networks were introduced to overcome the limitations of traditional RNNs. They have three gates—input, forget, and output gates—which help the network to learn and forget information over time.
How LSTM Works
Input Gate: The input gate decides which information from the current input should be stored in the cell state.
Forget Gate: The forget gate decides which information from the previous cell state should be forgotten.
Output Gate: The output gate decides which information from the cell state should be output as the result of the LSTM unit.
Example
Let's say we want to predict the next word in a sentence using LSTM. We will feed the sentence into the LSTM network, and it will learn the dependencies between the words.
Resources
For further reading on LSTM networks, you can check out our Introduction to RNNs tutorial.