Recurrent Neural Network

 Varshini P
5th Sem BCA, NCMS

Recurrent Neural Network is a type of artificial neural network, commonly used in speech recognition and Natural language processing. Recurrent neural networks are designed to recognize a data’s sequential characteristics and use patterns to produce the next likely scenario. Recurrent Neural Networks are used in deep learning and in the development of  models that simulate the activity of neurons in the human brain. They are especially powerful in use cases  in which context is critical to predicting an outcome and are distinct from other types of artificial neural network because they use feedback loops to process a sequence of data, These feedback loops allow information to persist the effect is often described as memory.

Working of Recurrent Neural Networks

When we talk about traditional neural networks, all the outputs and inputs are independent of each other as shown in the below diagram:

But in the case of recurrent neural networks, the output from the previous steps is fed into the input of the current state. For instance, to predict the next letter of any word, or to predict the next word of the sentence, there is a need to remember the previous letters or the words and store them in some form of memory.The hidden layer is the one that remembers some information about the sequence

Pros of Recurrent Neural Network

• An RNN model is modeled to remember each information throughout the time which is very helpful in any time series predictor.
• Even if the input size is larger, the model size does not increase.
• The weights can be shared across the time steps.

Cons of Recurrent Neural Network

• Due to its recurrent nature, the computation is slow.
• Training of RNN models can be difficult.

If we are using relu or tanh as activation functions, it becomes very difficult to process sequences that are very long.