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According to wikipedia of backpropagation: In fitting a neural network, backpropagation computes the gradient of the loss function during supervised learning with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. ...


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The classical version of the universal approximation theorem states that, roughly, given a continuous function $f \colon [0, 1]^n \to [0, 1]^n$, there exists a single layer neural network and a set of weights and biases such that this network approximates the given function $f$ arbitrarily well. It doesn't say anything about how you obtain such weights: the ...


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Yes, it is possible. What you have shown in case of ANN is what happens in a regression model using NNs. What you have shown in case of RNN is what happens when you are doing sequence-to-sequence translation (like French to English). If you want to get single values like in case of ANN, suppose you are doing regression, then, in the end, you will flatten the ...


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I would recommend using a larger dataset. The MNIST or EMNIST datasets are great for training OCRs. Also, how many generations are you training your network for.


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