[machine learning]ng lecture
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- cost function - cal the error of the hypotheses and the true value (sum)
- gradient descent - by gradient descent step by step we can reach the minimum
- logistic regression -
- classification
- hypothesis representation
- decision boundary
- cost function
- regularization
- overfitting -> reduce the weight
- cost function
neural networks :
- non-linear hypothesis
- minimization of cost function
backpropagation algo)(from the training of the neuron network, from wiki:)
第1阶段:激励传播
每次迭代中的传播环节包含两步:
(前向传播阶段)将训练输入送入网络以获得激励响应;
(反向传播阶段)将激励响应同训练输入对应的目标输出求差,从而获得隐层和输出层的响应误差。
第2阶段:权重更新
对于每个突触上的权重,按照以下步骤进行更新:
将输入激励和响应误差相乘,从而获得权重的梯度;
将这个梯度乘上一个比例并取反后加到权重上。
这个比例(百分比)将会影响到训练过程的速度和效果,因此称为“训练因子”。梯度的方向指明了误差扩大的方向,因此在更新权重的时候需要对其取反,从而减小权重引起的误差。第1和第2阶段可以反复循环迭代,直到网络的对输入的响应达到满意的预定的目标范围为止。
- random initialzation
- design of the system for machine learning: error analysis
- SVW (看不懂)
- clustering : kmean ; random initialization: # of clusering
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