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however convolutional layers can work on higher dimensional data as well in fact the most famous successes of convolutional neural networks are applying .

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so what is deep learning and cnn according to the 2014 rss keynote speech by andrew ng deep learning is more or less a brand name for all works related .

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from nips 2017 deep learning practice and trends tutorial stacked two 3x3 layers captures the same information as a single 5x5 layer with less parameters .

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understanding convolution the core of convolutional neural networks .

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as to my knowledge no one has yet come up with a solid mathematical proof that it works at all anyway imagine a large neural net with many hidden layers .

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cnns use a high overhead feature learning process it generates weights used in a classification process that can identify multiple items .

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multitask learning with weight sharing between multiple deep neural networks in this figure two related tasks are trained simultaneously using the network .

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in the previous example we fed the output of our convolutional layer into a fully connected layer but we can also compose two convolutional layers .

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an example convolutional neural network with multiple convolutional and activation layers .

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next the authors discuss how high resolution images are very important to get a higher level of accuracy but cause training time to jump drastically .

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one of today s most successful machine learning techniques is called deep learning the broad interest in deep learning is backed by some remarkable results .

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convolutional layer 76 3 1 convolutional neural network convolutional neural networks cnns are biologically inspired variants of multi layer perceptrons .

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the network consists of multiple layers of feature detecting neurons each layer has many neurons that respond to different combinations of inputs from .

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download full size image fig 3 the structure of the multi label convolutional neural network .

however deep learning works for imagery because an image is structured data and large quantities of training data .

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you can see how the filter maps a set of points from the input matrix to a single node in the next layer here is a more low level diagram of a convolution .

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