The name of LSTM refers to the analogy that a standard RNN has both "long-term memory" and "short-term memory". For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, speech activity detection, robot control, video games, and healthcare. This characteristic makes LSTM networks ideal for processing and predicting data. Such a recurrent neural network (RNN) can process not only single data points (such as images), but also entire sequences of data (such as speech or video). Unlike standard feedforward neural networks, LSTM has feedback connections. Long short-term memory ( LSTM) is an artificial neural network used in the fields of artificial intelligence and deep learning.
The Long Short-Term Memory (LSTM) cell can process data sequentially and keep its hidden state through time.
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