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It would be useful to provide the loss over time for both training and testing data set. From your description, it seems like you can minimize the training loss, but the testing performance is not going well. If that is the case, try to regularize the model more. One useful approach is doing data argumentation more. From the comments, it seems we have ...


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Why does this allow the training of deep networks, escaping network saturation at deep levels? We can treat a layer as a function, and adding a layer(with more parameters) leads to a new function with a larger hypothesis space. There are two methods to adding a layer, and for the generic method, we just add a layer and this would result in the spaces ...


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I think it's a reasonable claim that all graph convolutional networks are graph neural networks, since they operate on graphs, and are NNs. However, there are graph neural networks which don't use graph convolutions. For example, graphRNN is a generative neural network for graphs where an RNN is given all the previous nodes and edges, and decides whether or ...


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Might want to stratify the data based on those demographic variables to find a small set of "food patterns," simple frequency distributions of food. Once the patterns are selected, re-stratify by pattern. Combine the subject logs within each pattern to estimate a Markov chain of meal choices. Voila! one-ahead meal predictions. Given enough ...


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Your example is just broadcast semantics. This is easy to do using PyTorch, and probably Keras or Tensorflow (but I don't use them). x = torch.FloatTensor([[[0.0, 1.0], [0.0, 0.0], [1.0, 0.0], [0.0, 1.0]]]) y = torch.FloatTensor([[[0.43913543], [0.0], [-1.3466451], [0.43913543]]]) z = x * y print(z) Yields tensor([[[ 0.0000, 0.4391], [ 0.0000, 0.0000]...


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Very late but I think the real answer is more historical. Historically, deep learning refers to networks that use backpropagation in contrast to a lot of other types of neural network (Kohonen, mono-layer perceptron, oscillatory network, chaotic network, etc., etc.) that are a lot of research branches. When deep learning appears, before being the well-known ...


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You'd concatenate the images together in different channels and the neural network would interpret it the same way it would an RGB image. This can even be done if the images were 3D, using the Conv3D and MaxPooling3D layers. Convert the DICOMs to NIFTIs using dcm2niix (a terminal program), read them in using nibabel, convert them to numpy arrays, and ...


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How should I approach my particular case, where I have multiple images as input parameters? One option is to use a CNN for each image, and the flatten and concatenate the results to make predictions. The CNNs can be the same or different, depending on what your goals are.


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