How do recurrent neural networks work?

Tiara Williamson
Tiara WilliamsonAnswered

In Recurrent Neural Networks, the input layer x receives and processes the input to the neural network before passing it to the intermediate layer, hidden though a loop

The middle layer h may have numerous hidden layers, each having its own activation functions, weights, and biases. If the various parameters of successive hidden layers of a neural network are not impacted by the preceding layer (i.e., the neural network lacks memory), then a Recurrent Neural Network may be used.

The Recurrent Neural Network will normalize the various activation functions, weights, and biases such that every hidden layer has identical characteristics. Rather than constructing numerous hidden layers, it will generate one and iterate over it as often as necessary.

RNN deep learning was invented because the feed-forward neural network has a few flaws:

  • Does not support consecutive information.
  • Only considers the current input.
  • Cannot recall prior inputs.

The RNN recurrent algorithm solves these. An RNN in Machine Learning is capable of processing sequential data, accepting both the current input data and previously received inputs. Due to their internal memory, RNNs may remember past inputs.


  • Text Captioning. RNNs are used for picture captioning by assessing the existing activities.
  • Time Series Forecast. An RNN can tackle any time series issue, like forecasting the stock values for a certain month.
  • Organic Language Processing. An RNN for Natural Language Processing may be used to do Text Mining and Sentiment Analysis (NLP).
  • Automatic Translation. Given a single-language input, RNNs may be used to convert the input into many languages.

Recurrent Neural Network Types

  • One to One. This is also referred to as a Vanilla Neural Network. It is used for common Machine Learning tasks with a single input and output.
  • One to Many. This receives a single input and produces numerous outputs. This is shown by the picture caption.
  • Many to One. This accepts a series of inputs and produces a single output. An excellent example of this type of network is sentiment analysis, in which a given line may be categorized as having positive or negative emotions.
  • Many to Many. This receives a series of inputs and produces a series of outputs. Translation by machine is one of the instances.
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