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: | ||
