神经网络
From Ufldl
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【原文】To train this network, we would need training examples <math>(x^{(i)}, y^{(i)})</math> where <math>y^{(i)} \in \Re^2</math>. This sort of network is useful if there're multiple outputs that you're interested in predicting. (For example, in a medical diagnosis application, the vector <math>x</math> might give the input features of a patient, and the different outputs <math>y_i</math>'s might indicate presence or absence of different diseases.) | 【原文】To train this network, we would need training examples <math>(x^{(i)}, y^{(i)})</math> where <math>y^{(i)} \in \Re^2</math>. This sort of network is useful if there're multiple outputs that you're interested in predicting. (For example, in a medical diagnosis application, the vector <math>x</math> might give the input features of a patient, and the different outputs <math>y_i</math>'s might indicate presence or absence of different diseases.) | ||
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【初译】为了训练网络,我们需要训练样本 <math>(x^{(i)}, y^{(i)})</math> ,其中<math>y^{(i)} \in \Re^2</math>。当你想用于预测时,多个输出的网络更为有用。(例如,在医学诊断应用中,向量<math>x</math> 表示病人情况,不同输出<math>y_i</math>表示不同疾病的存在或者不存在。) | 【初译】为了训练网络,我们需要训练样本 <math>(x^{(i)}, y^{(i)})</math> ,其中<math>y^{(i)} \in \Re^2</math>。当你想用于预测时,多个输出的网络更为有用。(例如,在医学诊断应用中,向量<math>x</math> 表示病人情况,不同输出<math>y_i</math>表示不同疾病的存在或者不存在。) | ||
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forward propagation 正向传播(这里为了与“反向传播”的翻译相对应,采用“正向传播”) | forward propagation 正向传播(这里为了与“反向传播”的翻译相对应,采用“正向传播”) | ||
feedforward neural network 前馈神经网络(参照Mitchell的《机器学习》的翻译) | feedforward neural network 前馈神经网络(参照Mitchell的《机器学习》的翻译) | ||
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+ | {{Sparse_Autoencoder}} |