Lstm formula

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Chain rule refresher ¶. As seen above, foward propagation can be viewed as a long series of nested equations. If you think of feed forward this way, then backpropagation is merely an application of Chain rule to find the Derivatives of cost with respect to any variable in the nested equation.

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3.1. Long Short Term Memory Networks Long Short Term Memory Networks were introduced by [8], in which the main framework is the same as RNNs, but the internal circular module is designed in a different structure. The key to LSTM networks is the cell state and four control-ling gates which are called as the forget gate, the input gate,

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Mar 21, 2019 · We propose an ensemble of long–short‐term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible ...

LSTM can be used to solve problems faced by the RNN model. Gates in LSTM are the sigmoid activation functions i.e they output a value between 0 or 1 and in most of the cases it is either 0 or 1.The LSTM architectures are usually trained in a batch setting in the literature, where all data instances are present and processed together To calculate the weights in (22), we use the following formula.