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LSTM

What is LSTM?

An LSTM (Long Short-Term Memory) network is a kind of recurrent neural network (RNN) that has the ability to learn order dependence in sequence prediction problems. In addition to the standard units, LSTM networks use special units. They also have memory cells that store information in memory for large durations.

Let’s say that you’re trying to classify the events occurring in different parts of a novel. As a human, you would use the previous events that occurred in the novel to understand the events that are currently happening. But a traditional neural network is not capable of doing that.

That’s where recurrent neural networks come into play. 

Recurrent neural networks have loops built into them that allow information to persist. These loops essentially let information travel from one step of the network to the next one.

But there is one issue with normal RNNs. Let’s go back to the example of a novel. If an early part of the novel it is mentioned that Jack is a powerlifter and much, much later there is a mention of Jack getting ready for a competition, a normal RNN would not be able to figure out what Jack would be competing in. 

If there is a large gap between the point where the RNN first finds information and the point when it needs that information, the RNN might not be able to connect that information. Theoretically, they should be able to do this, but it does not tend to happen in practical situations.

LSTM (Long Short-Term Memory) Networks are specifically designed to solve this problem. Instead of a single neural network layer, they have four neural network layers.


Why are LSTMs better than standard RNNs?

LSTMs actually deliver on the promise made by RNNs. Standard RNNs cannot access as vast a range of contextual information as an LSTM network can. The influence of inputs on the hidden layer, and the network output, tends to either decay or blow up exponentially as it cycles through the network’s recurrent connections in an RNN.

LSTMs triumph over the challenges of vanishing gradients and exploding gradients. In addition to this, LSTMs also give you more control, which means you get higher-quality results.

In most instances where recurrent neural networks performed remarkable feats and achieved great results, the kind of recurrent neural network used was an LSTM network.

LSTMs do tend to be more effective than standard RNNs, but they also happen to be more complex and carry higher operating costs.


The applications of LSTMs

LSTMs can handle many tasks that standard recurrent neural networks cannot. It has been proven to be extremely effective in performing speech recognition, language modeling, and machine translation tasks.

They have also been useful for protein secondary structure prediction, handwriting recognition and generation, and even for analyzing audio and video data.

LSTMs have also been used in rhythm learning, music composition, human action recognition, sign language translation, time series prediction & anomaly detection, object co-segmentation, and robot control.

They have even been applied in semantic parsing, traffic forecasting (in the short term), drug design, and airport passenger management.

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LSTM

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is LSTM?

An LSTM (Long Short-Term Memory) network is a kind of recurrent neural network (RNN) that has the ability to learn order dependence in sequence prediction problems. In addition to the standard units, LSTM networks use special units. They also have memory cells that store information in memory for large durations.

Let’s say that you’re trying to classify the events occurring in different parts of a novel. As a human, you would use the previous events that occurred in the novel to understand the events that are currently happening. But a traditional neural network is not capable of doing that.

That’s where recurrent neural networks come into play. 

Recurrent neural networks have loops built into them that allow information to persist. These loops essentially let information travel from one step of the network to the next one.

But there is one issue with normal RNNs. Let’s go back to the example of a novel. If an early part of the novel it is mentioned that Jack is a powerlifter and much, much later there is a mention of Jack getting ready for a competition, a normal RNN would not be able to figure out what Jack would be competing in. 

If there is a large gap between the point where the RNN first finds information and the point when it needs that information, the RNN might not be able to connect that information. Theoretically, they should be able to do this, but it does not tend to happen in practical situations.

LSTM (Long Short-Term Memory) Networks are specifically designed to solve this problem. Instead of a single neural network layer, they have four neural network layers.


Why are LSTMs better than standard RNNs?

LSTMs actually deliver on the promise made by RNNs. Standard RNNs cannot access as vast a range of contextual information as an LSTM network can. The influence of inputs on the hidden layer, and the network output, tends to either decay or blow up exponentially as it cycles through the network’s recurrent connections in an RNN.

LSTMs triumph over the challenges of vanishing gradients and exploding gradients. In addition to this, LSTMs also give you more control, which means you get higher-quality results.

In most instances where recurrent neural networks performed remarkable feats and achieved great results, the kind of recurrent neural network used was an LSTM network.

LSTMs do tend to be more effective than standard RNNs, but they also happen to be more complex and carry higher operating costs.


The applications of LSTMs

LSTMs can handle many tasks that standard recurrent neural networks cannot. It has been proven to be extremely effective in performing speech recognition, language modeling, and machine translation tasks.

They have also been useful for protein secondary structure prediction, handwriting recognition and generation, and even for analyzing audio and video data.

LSTMs have also been used in rhythm learning, music composition, human action recognition, sign language translation, time series prediction & anomaly detection, object co-segmentation, and robot control.

They have even been applied in semantic parsing, traffic forecasting (in the short term), drug design, and airport passenger management.

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