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What is Long Short Term Memory (LSTM) ?
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in deep learning. It can process not only images, but also entire sequences of data like speech and videos. Typically, recurrent neural networks have “short-term memory” in that they use persistent past information for use in the current neural network. Essentially, the previous information is used in the current task. This means that we do not have a list of all of the previous information available for the neural node.
Architecture of LSTM
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in deep learning. It can process not only images, but also entire sequences of data like speech and videos. Typically, recurrent neural networks have “short-term memory” in that they use persistent past information for use in the current neural network. Essentially, the previous information is used in the current task. This means that we do not have a list of all of the previous information available for the neural node.
Architecture of LSTM
LSTMs have both Long Term Memory (LTM) and Short Term Memory (STM), and the concept of gates is used to make the calculations simple and effective.
Forget Gate: When LTM enters the forget gate, it discards information that is not helpful.
Input Gate : Controls what new information is added to cell state from current input
Output : Conditionally decides what to output from the memory
This model is able to learn long term dependencies. The first LSTM block takes the initial state of the network and the first time step of the sequence X(1), and computes the first output h(1) and the updated cell state c(1). At time step t, the block takes the current state of the network (c(t−1), h(t−1)) and the next time step of the sequence X(t), and computes the output ht and the updated cell state ct.
Application Of LSTM
Image captioning
Music generation
Language Translation
Handwritten Recognition
Time series prediction
Speech recognition
Semantic parsing
Short term traffic forecast
Handwritten generation
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