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Note: In Keras, every SimpleRNN has only three different weight matrices, and these weights are shared between all input cells; In other words, for all five cells in your network: \begin{align} h_t = tanh( w_{h} h_{t-1} + w_{x} x_{t} + b_h)\ ; t= 1..5 \end{align} For a deeper understanding of recurrent networks in Keras, you may want to read this eloquent ...


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These are a few approaches I found in the research field that combines both RNN and Reinforcement Learning that looks promising Reinforcement learning with LSTM networks Reinforcement learning with RNN Hybrid RNN approach Research paper links A Reinforcement Learning and Recurrent Neural Network Based Dynamic User Modeling System Supervised Reinforcement ...


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If you are evaluating point estimates (i.e. single number estimates) then you are well advised to use a proper scoring rule. Some metrics elicit "honest" estimates whereas others do not. A characterization of proper scoring rules was provided by Savage. Most people go with squared error, logarithmic. Contest sites like M-Competitions or ...


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A Convolutional Neural Network (CNN) learns the spatial relationships in the data that are associated with the target values. In text data, the spatial relationships are which words are near each other. A specific example is the "changing on the fly" is associated with the rec.sport.hockey category. Recurrent Neural Network (RNN) learns single ...


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