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Therefore, this function can be used as the loss function of logistic regression classifier. If the calculated value is closer to 1, then it means that it is closer to the world result, otherwise, it is farther away. If there is a three-layer neural network with input layer, hidden layer and output layer, we can calculate the partial derivative of the weight of the loss function. If we first calculate the partial derivative of the weight of the first layer, and then calculate the partial derivative of the weight of the second layer, we will find that there are some problems A lot of repeated calculation steps, like the example of simple function above, so, in order to avoid this kind of consumption, we use to find the partial derivative from the back to the front, find out the function value of each unit, find out the partial derivative of the corresponding unit, save it, multiply it all the time, and input the layer. The essence of calculation is from top to bottom.

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