Recurrent Neural Network

In the last two posts we introduced the core concepts of Deep Learning, Feedforward Neural Network and Convolutional Neural Network. In this post, we will have a look at two other popular deep learning techniques: Recurrent Neural Network and Long Short-Term Memory.

Recurrent Neural Network

The main difference to the previously introduced Networks is that the Recurrent Neural Network provides a feedback loop to the previous neuron. This architecture makes it possible to remember important information about the input the network received and takes the learning into consideration along with the next input. RNNs work very well with sequential data such as sound, time series (sensor) data or written natural languages.

The advantage of a RNN over a feedforward network is that the RNN can remember the output and use the output to predict the next element in a series, while a feedforward network is not able to fed the output back to the network. Real-time gesture tracking in videos is another important use-case for RNNs.

A Recurrent Neural Network
A Recurrent Neural Network

Long Short-Term Memory

A usual RNN has a short-term memory, which is already great at some aspect. However, there are requirenments for more advanced memory functionality. Long Short-Term Memory is solving this problem. The two Austrian researchers Josef Hochreiter and Jürgen Schmidhuber introduced LSTM. LSTMs enable RNNs to remember inputs over a long period of time. Therefore, LSTMs are used in combination with RNNs for sequential data which have long time lags in between.

LSTM learns over time on which information is relevant and what information isn’t relevant. This is done by assigning weights to information. This information is then assigned to three different gates within the LSTM: the input gate, the output gate and the “forget” gate.

This tutorial is part of the Machine Learning Tutorial. You can learn more about Machine Learning by going through this tutorial. On Cloudvane, there are many more tutorials about (Big) Data, Data Science and alike, read about them in the Big Data Tutorials here. If you look for great datasets to play with, I would recommend you Kaggle.

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