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Notes on Backpropagation

Notes on BackpropagationPeter SadowskiDepartment of Computer ScienceUniversity of California IrvineIrvine, CA document derives Backpropagation for some common neural Cross Entropy Error with Logistic ActivationIn a classification task with two classes, it is standard to use a neural network architecture witha single logistic output unit and the cross-entropy loss function (as opposed to, for example, thesum-of-squared loss function). With this combination, the output prediction is always between zeroand one, and is interpreted as a probability. Training corresponds to maximizing the conditionallog-likelihood of the data, and as we will see, the gradient calculation simplifies nicely with can generalize this slightly to the case where we have multiple, independent, two-class classi-fication tasks.

Notes on Backpropagation Peter Sadowski Department of Computer Science University of California Irvine Irvine, CA 92697 peter.j.sadowski@uci.edu Abstract

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