Hidden latex layers

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A feed-forward neural network applies a series of functions to the data. In other words, these will light up when the eye detectors and nose detectors from the previous layer turn on with the right patterns. In this paper by some Google folks with one of the world's largest neural nets, they show a "face detector" neuron and a "cat detector" neuron this way, as well as a second way: The second approach is nice because it shows how flexible and nonlinear the network is--these high-level "detectors" are sensitive to all these images, even though they don't particularly look similar at the pixel level. The output of the hidden layer could be different things, but for now let's suppose that they come out of sigmoidal activation functions.

The output of the hidden layer could be different things, but for now let's suppose that they come out of sigmoidal activation functions. A feed-forward neural network applies a series of functions to the data. By using our site, you acknowledge that you have read and understand our Cookie Policy , Privacy Policy , and our Terms of Service. I understand the input layer and how to normalize the data, I also understand the bias unit, but when it comes to the hidden layer, what the actual computation is in that layer, and how it maps to the output is just a little foggy. Because it has attracted low-quality or spam answers that had to be removed, posting an answer now requires 10 reputation on this site the association bonus does not count.

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