What is LSTM in R

This recipe explains what is LSTM in R

Recipe Objective - What is LSTM in R?

The Long short-term memory neural network is a variant of recurrent neural networks that is it provides a way to carry the information across many timesteps. Long short-term memory saves the information for later thus it prevents older signals from gradually vanishing during processing. The units of an LSTM neural network are used as the building units for the layers of a Recurrent neural network often called as LSTM network. The LSTM can read, write and delete information from its memory. The memory in long short-term memory can be seen as the gated cell with gated meaning cell decides whether or not to store the information (i.e if it opens gates or not) which is based on the importance it assigns to information. The assigning of importance happens through the weights which are learned by the LSTM algorithm. This simply means that LSTM learns over time by differentiating what information is important and what is not.

This recipe explains what are Long short-term memory neural networks and how they are executed.

Explanation of Long short-term memory.

Long short-term memory have three gates that is input, forget and the output gate. These gates determine whether or not to let new input in the input gate and delete the information as it is not important (forget gate) or let it leave it to impact the output at the current timestep (output gate). Long short-term memory gates are analog in the form of sigmoids that is it ranges from zero to one. The fact that they are analog enables them to execute backpropagation.

Long short-term memory solves problematic issues of vanishing gradients as it keeps the gradients steep enough which in turn keeps the training relatively short and finally the accuracy high.

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