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RNN is not able to retain memory that are from far back in the past because of the vanishing gradient problem (i.e. the gradient from backpropagation is unable to reach the earlier states). This is a limitation of the model itself. Thus, we need to introduce a more powerful model, i.e. lowering the bias (in the expense of increasing the variance). ...


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The input of an LSTM is a sequence of vectors. In your case, each of these vectors represents a word encoded as a one-hot vector. One-hot encoding is a way to express a discrete element (e.g. a word) numerically. Each one-hot vector is a vector of length $d$, where $d$ is the total number of words we can represent, and where all positions in the vector are 0 ...


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There are a couple of design patterns that are contributing to slow code: Pandas is not designed for large-scale, fast data processing. Your code is using a for loop which can be slow. You are manifesting the sliding window before the program needs it. It might be better to create a view on the data and then manifest the data in memory only when the ...


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