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@Sammy below are the 2 images which i have mentioned in comment where im unable to find yhat =Way+b since our goal is to find y ,how can Way(weights * y) come into picture for prediction of Y ?


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Keep in mind how matrix multiplication works with regards to the dimensions: Multiplying a matrix with dimensions $n,m$ by a matrix with dimensions $m,k$ results in a matrix of size $n,k$. Therefore, you can add as many rows as you like to the second matrix with no change to the shape of the result of the matrix multiplication. But of course the first ...


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o(t) is not the result of concatenation of h(t-1) and x(t), but a simple matrix multiplication. See wikipedia for further details: https://en.wikipedia.org/wiki/Long_short-term_memory


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In Keras, with verbose=1 (default parameter of the fit method) will display the total number of samples, not the batch number. If your batch size is 128, then the progress bar will jump by multiples of 128. You can try to change batch_size parameter to 13714, and you will see the progress bar jumping straight from 0/13714 to 13714/13714, since you would ...


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Don't get hung up on the word "govern" here. $W_{ax}$, $W_{ay}$ and $W_{aa}$ are simply the weights and they play in principle the same role weights play in feed forward network (except that feedforward networks do not have $W_{aa}$): $W_{ax}$ are the weights from your input layer to the first hidden layer (just as they are in feedforward networks) $W_{ay}$ ...


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Maybe somebody will have a better idea but the default method would be to generate a set of names, then ask a few annotators to label them as good or bad (possibly scoring them from 1 very bad to 5 very good), and finally train a supervised model to recognize the good from the bad ones. This approach would also give you the opportunity to check the inter-...


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Considering that Neural Networks (LSTM-RNN in this case), like the rest of deep learning methods, are like black boxes. The transformation applied are too complex to determine how much a value, variable, weight affects the solution. Although is a hot topic, there's no current method viable or wide-known used to understand which variables are better or ...


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It is not a good idea to keep the statefulness like your second figure, because this would prevent the parallelization of the computations. A stateful RNN (LSTM, GRU, etc) saves the last hidden state and uses it as the initial state for the next batch. If you do it like in your first figure, all sentences in a batch can be computed in parallel. However, for ...


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This flag is used to have truncated back-propagation through time: the gradient is propagated through the hidden states of the LSTM across the time dimension in the batch and then, in the next batch, the last hidden states are used as input states for the LSTM. This allows the LSTM to use longer context at training time while constraining the number of ...


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https://pjreddie.com/darknet/ is their website... I cite : "Darknet: Open Source Neural Networks in C Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation." As to why they used that, well it's open source and in C, which are good points and seems to be performant (see ...


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To remove the noise I'd suggest you utilize Fourier Transformation to get the frequency spectrum of the signal. Then you can apply a threshold to remove high-frequency signal components which are mainly noises. As I understood you are using python. The code below creates an artificial noisy signal and removes it using FFT (Fast Fourier Transformation) ...


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I am pleased to provide the direct usage of kernel, recurrent_kernel and bias in the following expression. I am wondering the Keras usage is quite rare in the programming language. Shall some one give an exact explaining to the array expression . ### kernel--weights between x_{t} and units W_i = W[:, :units] W_f = W[:, units:units * 2] W_c = W[:, units *...


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seq2seq is a type of problem, that can be solved with LSTM, RNN etc... Generally one thinks about sequences of data that are inter connected and these relationships in sequences should be modeled (which can be achieved with for example LSTM etc.)


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There is no explicit notion of memory (like gates in lstm and gru) Gates are a way to optionally let information through, ommiting this functionality we will just be updating weights that will in process fade away hence longer memory is hard to learn.


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this answer may be late, but one really good way of going about this problem would be to develop a transformer network, read the paper " attention is all you need " by google, there are a lot of datasets available online, you can either use them or you can create your own dataset by crawling Reddit or some other social networking site or anything, focus ...


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