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What are recurrent neural networks?

Lucindah Fry-Nartey

A recurrent neural network (RNN) is a class of artificial neural networks that contains loops within its hidden layers, and allows information to persistremain within the network over time. More specifically, a recurrent neural network is a deep learning technique and can be used in various applications.

Traditional Neural Network | Recurrent Neural Network

Unlike traditional neural networks, which utilize a simple feed-forward flow, recurrent neural networks contain a looping mechanism that computes an internal state update with each time step. This gives an RNN the capability to retain information about preceding elements in a series of data. This is particularly useful because it allows the RNN to handle varied lengths of data, maintain the order within a sequence of data, and share parameters across neurons in the network. Recurrent neural networks operate based on a recurrence relation that is given by:

We can see from the recurrence relation that the internal state at a given time step is dependent on the internal state at the previous time step and the current input.

For example, if we take the activation function to be tanh, then the internal state at time t can be computed as tanh applied to the summation of the previous internal state and the current input vectoreach multiplied by their respective weights. This can be expressed mathematically by the equation:

The output at the time t would therefore be a multiplication of the internal state at that time by the separate weight.

Recurrent neural networks are mainly used to analyze sequential datalike time-series data, audio, and video or structured datasuch as spatial or graphical data. Applications of RNNs include:

  • Speech recognition
  • Natural language processing
  • Video captioning
  • Text processing
  • Language Translation

We can see from the examples of applications above that recurrent neural networks are particularly useful for addressing issues where the order of the input data is of great importance.

# initialize the neural network and the hidden layers 
my_rnn = RNN()
hidden_layers = [0, 0, 0, 0]

sequence = ["I", "love", "recurrent", "neural"] #example data sequence

# feeding the sequence as input to the network
for word in sequence:
    prediction, hidden_layers = my_rnn(word, hidden_layers)

# prediction for the next word
next_word = prediction
Pseudo code for RNN operation [Source: MIT 6.S191]
  • Lines 2 and 3 initialize the RNN and the hidden layers within the network.

  • Line 6 sets up the input data.

  • Lines 9 and 10 loop through the sequence and feed both the current word and the previous internal state into the RNN. A prediction is then made and the internal state is updated.

  • Line 13 returns the output of the RNN, which is the predicted word.



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