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Recurrent neural network - Wikipedia

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A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the other type, the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs[1][2][3] makes them applicable to tasks such as unsegmented, connected handwriting recognition[4] or speech recognition.[5][6] The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior.[7] A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural

Recurrent neural network - Wikipedia Jump to content From Wikipedia, the free encyclopedia Class of artificial neural network Not to be confused with Recursive neural network or Feedback neural network . Part of a series on Machine learning and data mining Paradigms Supervised learning Unsupervised learning Semi-supervised learning Self-supervised learning Reinforcement learning Meta-learning Online learning Batch learning Curriculum learning Rule-based learning Neuro-symbolic AI Neuromorphic engineering Quantum machine learning Problems Classification Generative modeling Regression Clustering

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