Data Science: Recurrent Neural Network (Definitions)

"A neural net with feedback connections, such as a BAM, Hopfield net, Boltzmann machine, or recurrent backpropagation net. In contrast, the signal in a feedforward neural net passes from the input units (through any hidden units) to the output units." (Laurene V Fausett, "Fundamentals of Neural Networks: Architectures, Algorithms, and Applications", 1994)

"A neural network topology where the units are connected so that inputs signals flow back and forth between the neural processing units until the neural network settles down. The outputs are then read from the output units." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Networks with feedback connections from neurons in one layer to neurons in a previous layer." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"RNN topology involves backward links from output to the input and hidden layers." (Siddhartha Bhattacharjee et al, "Quantum Backpropagation Neural Network Approach for Modeling of Phenol Adsorption from Aqueous Solution by Orange Peel Ash", 2013)

"Neural network whose feedback connections allow signals to circulate within it." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"An RNN is a special kind of neural network used for modeling sequential data." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior." (Udit Singhania & B. K. Tripathy, "Text-Based Image Retrieval Using Deep Learning", 2021)

"A RNN [Recurrent Neural Network] models sequential interactions through a hidden state, or memory. It can take up to N inputs and produce up to N outputs. For example, an input sequence may be a sentence with the outputs being the part-of-speech tag for each word (N-to-N). An input could be a sentence, and the output a sentiment classification of the sentence (N-to-1). An input could be a single image, and the output could be a sequence of words corresponding to the description of an image (1-to-N). At each time step, an RNN calculates a new hidden state ('memory') based on the current input and the previous hidden state. The 'recurrent' stems from the facts that at each step the same parameters are used and the network performs the same calculations based on different inputs." (Wild ML)

"Recurrent Neural Network (RNN) refers to a type of artificial neural network used to understand sequential information and predict follow-on probabilities. RNNs are widely used in natural language processing, with applications including language modeling and speech recognition." (Accenture)