Recurrent Neural Networks (RNNs) refer to the network class with an infinite impulse response. It is a type of neural network, and it has three layers include the input layer, multiple hidden layers, and the output layer. This type of network allows exhibiting temporal dynamic behavior.

The advantage of RNN is modeling data collection in which each element is dependable on previous ones and provides predictive results on sequential data, which is not performed by other algorithms. The input layer accepts the input, the first level hidden layer activations are applied, and these activations are sent to the next hidden layer, and successive activations through the layers produce the output. Each hidden layer is characterized by its weights and biases. With the successive activations through the layers producing the output, Recurrent Neural Networks can work well for the sequential data, and the complexity of the network is simple because the current data is only dependent on the previous data.

RNN contains an internal memory in which it stores the information of previous input to generate the next output of the sequence. Types of recurrent neural networks are Fully Recurrent Networks, Recursive Neural Networks, and Neural History Compressor.

The most popular applications of the recurrent neural network are prediction problems, video tagging, Medical data analysis, language modeling and generating text, generation of image descriptions, Text to speech recognition, time series problems, forecasting, image modeling, and language translation. Future directions of Recurrent neural networks are RNN using back-propagation through time algorithm, gating mechanism in RNN, unitary RNN, RNN regularization, three-dimensional medical image analysis such as head MRI scan, lung CT and abdominal MRI, and cancer detection and segmentation.

   • Deep Recurrent neural networks (RNNs) exploit high dimensional hidden states with non-linear dynamics, which are capable of learning features and long-term dependencies from sequential and time-series data.

   • Deep Recurrent Neural Networks can hierarchically capture the sequential nature of the text, and it is unique because they allow operation over a sequence of vectors over time.

   • DRNN disentangles variations of the input sequence and can adapt quickly to changing input nodes, and develops a more compact hidden state.

   • Deep Neural networks increase the computational complexity of the model due to more parameters.

   • Recurrent Neural Network meets the difficulty in capturing the long-term dependency due to the gradient vanishing with the back-propagation strategy while training the parameters.

   • RNNs gained immense popularity in various applications such as Vehicle Trajectory prediction, anomaly detection, and Data-driven traffic forecasting systems such as driver action predictions.