LSTMs Assignment Help

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What is LSTMs

LSTM stands for Short Term Long Term Memory. It is a model or an architecture that extends the memory of recurrent neural networks. 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.

Application Of LSTMs

  • Robot control

  • Time series prediction

  • Speech recognition

  • Rhythm learning

  • Music composition

  • Grammar learning

  • Handwriting recognition

  • Human action recognition

  • Sign language translation

  • Protein homology detection

  • Predicting subcellular localization of proteins

  • Time series anomaly detection

  • Several prediction tasks in the area of business process management

  • Prediction in medical care pathways

  • Semantic parsing

  • Object co-segmentation

  • Airport passenger management

  • Short-term traffic forecast

  • Drug design

Architecture Of LSTMs

​The functioning of LSTM can be visualized by understanding the functioning of a news channel’s team covering a murder story. Now, a news story is built around facts, evidence and statements of many people. Whenever a new event occurs you take either of the three steps.

  • Forget Gate

  • Input Gate

  • Output Gate

Forget Gate

A forget gate is responsible for removing information from the cell state. The information that is no longer required for the LSTM to understand things or the information that is of less importance is removed via the multiplication of a filter. This is required for optimizing the performance of the LSTM network.

Input Gate

The input gate is responsible for the addition of information to the cell state.

​Output Gate

This job of selecting useful information from the current cell state and showing it out as output is done via the output gate.