2014斯坦福大学-吴恩达公开课学习笔记

资源链接：链接: https://pan.baidu.com/s/1c1MIm1E 密码: gant

chapter2 : linear regression with one feature

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chapter4：linear regression with multiple feature

• 在我们面对多维特征问题的时候，我们要保证这些特征都具有相近的尺度，这将帮助梯度下降算法更快地收敛

   

• 如果学习率 α 过小，则达到收敛所需的迭代次数会非常高；如果学习率 α 过大，每次迭代可能不会减小代价函数，可能会越过局部最小值导致无法收敛。

        

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chapter 5 : Octave

• 参考文献 
• plot 

  x=[0:0.01:1];
  y1=sin(2*pi*x);
  plot(x,y1);
  y2=cos(2*pi*x);
  hold on;
  plot(x,y2);
  xlabel('time');
  ylabel('value');
  title('my plot');
  legend('sin','cos');
  print -dpng 'my.png';
  close;
  figure(1);plot(x,y1);
  figure(2);plot(x,y2);
  figure(3);
  subplot(1,2,1);
  plot(x,y1);
  subplot(1,2,2);
  plot(x,y2);
  axis([0.5 1 -1 1]); %change the axis of x and y
  clf;
  a=magic(5)
  imagesc(a);
  imagesc(a), colorbar,colormap gray;

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chapter 6 : logistic regression and regularization

• 学习笔记
• 
• for convex 
• 

 

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chapter 7 : regularization

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chapter 8 : neural network

• cost function

        

• forward propagation

      

• backward propagation

                

• 推倒视频链接

• 数学证明
• numerical estimation of gradient
• 
• random initialization and the step of training a neural network
• 

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chapter 10 : Deciding what to try next

• evaluating a hypothesis with cross validation
• 

•  Diagnosing bias and variance

• 
• learning curves and decide what to do next
• 

• 高手学习笔记

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chapter 11 : precision and recall 

• skewed data vs precision and recall
• 

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chapter 12 : SVM 

• for enlagering the projection of X, then we get large margin 
• 
• kernel and  do perform feature scalling before using the Gaussian kernel
• 

• Kernel need to satisfy technical condition called "Mercer's Theorem"  to make sure SVM packages' optimizations run correctly, and do not diverge.

• Polynomial kernel: (X^TL+constant)^degree

• SVM  has a convex optimization problem

•  大牛学习博客

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chapter 13 : Unsupervised learning and clustering

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chapter 14 : PCA

•   first perform mean normalization and feature scalling so that the feature should have zero mean and comparabel ranges of values.
• 
• Data preprocessing
• 
• PCA and SVD , the implementation of PCA , the choosing of K
• 
  
• getting the PCA parameter only on the training set and use them on the test and cross validation set

chapter 15 :