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I have kind of a similar dataset. I'll tell you what I did. I haven't got great results, but it may be due to my data being bad more than anything. Like @Leevo mentioned, you need to create a sliding window. You can do this with a simple for loop: def split_sequences(df, n_timesteps_in, n_timesteps_out): X, y = list(), list() for i in range(len(df)): ...


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As you suggest, the update rule for Adam is based only on the sign, not the magnitude of the gradient. Gradient clipping should not be needed to prevent exploding gradients when using Adam to optimize (though it might still be useful, depending on the circumstance).


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Linking to the same paper as @scholle but explaining the process differently (book and paper). You do not need to train the model multiple times. The algorithm described in the links above require a trained model to begin with. Given a trained model, compute the metric of interest on some dataset (the book discusses pros/cons of using training set vs test ...


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There is not such difference between zero padding & character padding ,as we applying padding to extract the edges & gradients to form the object for better learning with respect to human vision. Even with images mostly people use zero padding which creates black background but depending on the datasets & problem statement padding has to change ...


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