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The architecture of a RNN is called recurrent because it applies the same function at each step. So all the cells on the graph actually represent the same computation, but not the same state. Each green square in your figure represent the computation. $$s^{(t)} = f(s^{(t-1)}, x^{(t)}, \theta)$$ Where $f$ is the function of the RNN, $\theta$ are parameters, ...

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A neural language model tries to predict a conditional probability $P (w_{i + 1} | w_1, \dots, w_i)$. It approximates the probability with the following $P(w_{i+1} | s(w_1, \dots, w_i))$, where $s$ is a state function. After that an LSTM looked at all the words $w_1, \dots, w_i$, it has an updated state, so now it contains some useful information about all ...

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If you know that your trajectory has a certain parametric form then you can use methods that explore the parameter space for that form. Examples of such methods are Hough transform and custom-built moments. Hough transform maps a point in a real space into a manifold in the parameter space, and vice-versa, it maps a point in the parameter space into a line ...

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to completely receive you'r answer and to have a good insight visit : https://towardsdatascience.com/counting-no-of-parameters-in-deep-learning-models-by-hand-8f1716241889 g, no. of FFNNs in a unit (RNN has 1, GRU has 3, LSTM has 4) h, size of hidden units i, dimension/size of input Since every FFNN(feed forward neural network) has h(h+i) + h parameters, we ...

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