What is Recurrent Neural Network (RNN)
/ February 20, 2018

Neural networks are powerful learning models that achieve state-of-the-art results in a wide range of supervised and unsupervised machine learning tasks. They are suited especially well for machine perception tasks, where the raw underlying features are not individually interpretable. The use of recurrent neural networks are often related to deep learning and the use of sequences to evolve models that simulate the neural activity in the human brain. Overview of Recurrent Neural Network (RNN) The fundamental feature of a Recurrent Neural Network (RNN) is that the network contains at least one feed-back connection, so the activations can flow round in a loop. That enables the networks to do temporal processing and learn sequences, e.g., perform sequence recognition/reproduction or temporal association/prediction. Recurrent Neural Networks (RNNs) are connectionist models with the ability to selectively pass information across sequence steps, while processing sequential data one element at a time. Thus they can model input and/or output consisting of sequences of elements that are not independent. Further, recurrent neural networks can simultaneously model sequential and time dependencies on multiple scales. Figure 1: Recurrent Neural Network In other words, the RNN will be a function with inputs​$$x_t$$​  (input vector) and previous state ​$$h_(t-1)… Insert math as Additional settings Formula color Type math using LaTeX Preview \({}$$
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