What is RNN in neural networks

This recipe explains what is RNN in neural networks

Recipe Objective - What is RNN in neural networks?

Recurrent neural networks processes the sequences by iterating through the sequence elements and maintaining the state containing information relative to what it has seen so far usually. It functions just like human brain which contains a biological intelligence which processes the information incrementally while maintaining an internal model of what it’s processing and which is built from past information and constantly updated as new information comes in. RNN is a type of neural networks which consists of internal loop so that network loops internally over sequence elements. The state of the RNN is reset between the processing of two different independent sequences such as two different IMDB reviews so it still consider one sequence as a single data point that is a single input to the network.

This recipe explains what are Recurrent neural networks and how they are executed.

Complete Guide to Tensorflow for Deep Learning with Python for Free

Explanation of recurrent neural network.

In RNN, the information cycles through the loop that is when it makes a decision, it considers the current input and also what it has learned from the inputs it had received previously. In Feed-forward neural networks, it does not have memory of the input they receive and are bad at predicting the coming next as the feed-forward network only considers the current input, it does not have notion of order in time. It don't remember anything about what happened in the past except its training.

Recurrent neural network is mainly a sequence of neural networks that you train one after another with backpropagation. RNN has access to less information than the earlier baseline models in many neural network problems and simple rnn layer is not so good ar processing long sequences such as text.

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