神经网络
From Ufldl
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- | 【原文】Consider a supervised learning problem where we have access to labeled training examples <math>(x(^ i),y(^ i))</math>. Neural networks give a way of defining a complex, non-linear form of hypotheses <math>h_W,b(x)</math>, with parameters W,b that we can fit to our data. | + | 【原文】Consider a supervised learning problem where we have access to labeled training examples <math>(x(^ i),y(^ i))</math>. Neural networks give a way of defining a complex, non-linear form of hypotheses <math>h_W , b(x)</math>, with parameters W,b that we can fit to our data. |
【初译】处理监督学习问题时,我们使用了标记过的训练样本 <math>(x(^ i),y(^ i))</math>。神经网络提供了一个复杂的非线性假设函数<math>h_W,b(x)</math>,其中的参数W,b可以通过数据来调整。 | 【初译】处理监督学习问题时,我们使用了标记过的训练样本 <math>(x(^ i),y(^ i))</math>。神经网络提供了一个复杂的非线性假设函数<math>h_W,b(x)</math>,其中的参数W,b可以通过数据来调整。 |