Improvements thanks to #1 feedback #2
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Updating this in preparation of YouTube tutorial. Changes thanks to feedback from @nsthorat. I think if we are to create generic `NeuralNetwork` or `Classifier` classes in ml5, a lot of the work will inevitably involve data helper classes so that the end user can work with vanilla arrays and tensors are all created and managed internally by ml5.
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Some updates thanks to feedback from @nsthorat. I think if we are to create generic
NeuralNetworkorClassifierclasses in ml5, a lot of the work will inevitably involve data helper classes so that the end user can work with vanilla arrays and the tensors are all created and managed internally by ml5.I have a somewhat clear picture in my head of what a
Classifierclass might look like. . .with anImageClassifiersubclass with convolutional layers. In this XOR example I'm calling the classNeuralNetworkbut not sure I love this as a name. Perhaps XOR is not useful as an example for ml5 so this is moot. Maybe we have aClassifierand . .Predictor(for regression?). Hmmm.