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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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Your inputs have different length so as suggested by @skrrrt, you should pad your data and apply a mask into your model. The following pads all your input with 0. values so that all sequences have the same length. from tensorflow.keras.preprocessing.sequence import pad_sequences padded_inputs = pad_sequences(X, padding="post", dtype='float') You ...


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RNNs and CNNs are not mutually exclusive! It might seem that they are used to handle different problems, but it is important to note that some types of data can be processed by either architecture. For instance, RNNs uses the sequences as the input. It should be mentioned that sequences are not just limited to text or music. Sequences can also be videos, ...


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When I think about RNNs applied to Computer Vision, two main research areas of Deep Learning come up to my mind: Image Captioning: Neural Networks trained to produce descriptions of images. In that case, you have a Conv ecoder that processed pixel data, and an RNN decoder that produces a description. Video processing (I don't have a better term). Anything ...


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