反向传导算法
From Ufldl
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+ | 翻译者:王方,email:fangkey@gmail.com,新浪微博:@GuitarFang | ||
+ | 校对者:林锋,email: xlfg@yeah.net, 新浪微博:@大黄蜂的思索 | ||
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:【原文】: | :【原文】: | ||
Suppose we have a fixed training set <math>\{ (x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)}) \}</math> of <math>m</math> training examples. We can train our neural network using batch gradient descent. In detail, for a single training example <math>(x,y)</math>, we define the cost function with respect to that single example to be: | Suppose we have a fixed training set <math>\{ (x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)}) \}</math> of <math>m</math> training examples. We can train our neural network using batch gradient descent. In detail, for a single training example <math>(x,y)</math>, we define the cost function with respect to that single example to be: |