Do you think how Alexa and Siri respond to our vocal instructions?. The answer is with the help of Recurrent Neural Network.
RNN was first developed by John Hopfield in 1982.
The unique thing in RNN is that it can remember the previous input. And use these inputs to improves the accuracy of output. RNN can also predict the output of time series data.
For example, suppose in your music app, there are different genre of music is stored based on the day. Like on Monday, the music genre is Motivational, on Tuesday it’s Romantic, Wednesday is Classical, and so on. So when this information is given to RNN, the RNN can predict the playlist of other days based on the Monday playlist.
RNN can perform this kind of prediction task because it can store the previous inputs.
In RNN the output of the previous layer is used as an input of the current layer, using the same weights. RNN process the sequential or previously stored data repeatedly until the neural network learns.
RNN works on the Tanh activation function. It can predict the next word based on previous words.
If CNN gives machines the ability to see, RNN gives machines the ability to hear and understand language.
AppLication of RNN are-
Amazon Alexa.
Google Assistant.
Apple Siri.
SMS Autocomplete.
Natural Language Processing.
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