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A form of regularization useful in trainingneural networks. Dropout regularizationremoves a random selection of a fixed number of the units in a networklayer for a single gradient step. The more units dropped out, the strongerthe regularization. This is analogous to training the network to emulatean exponentially large ensemble of smaller networks.For full details, seeDropout: A Simple Way to Prevent Neural Networks fromOverfitting.

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A floating-point number that tells the gradient descentalgorithm how strongly to adjust weights and biases on eachiteration. For example, a learning rate of 0.3 wouldadjust weights and biases three times more powerfully than a learning rateof 0.1.

Obviously after fully factorizing a number into sorted list of prime factors the largest prime factor will be the last element in this list. In general case (for any random number) I don't know of any other ways to find out largest prime factor besides fully factorizing a number. 350c69d7ab