2014斯坦福大學-吳恩達公開課學習筆記

時間 2019-11-18

標籤斯坦福大學開課學習筆記简体版

原文原文鏈接

資源連接：連接: https://pan.baidu.com/s/1c1MIm1E 密碼: ganthtml

chapter2 : linear regression with one feature算法

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chapter4：linear regression with multiple feature函數

在咱們面對多維特徵問題的時候，咱們要保證這些特徵都具備相近的尺度，這將幫助梯度降低算法更快地收斂

若是學習率 α 太小，則達到收斂所需的迭代次數會很是高；若是學習率 α 過大，每次迭代可能不會減少代價函數，可能會越過局部最小值致使沒法收斂。

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chapter 5 : Octavespa

參考文獻

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;

View Code

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chapter 6 : logistic regression and regularization

學習筆記
for convex

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chapter 7 : regularization

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chapter 8 : neural network

cost function

forward propagation

backward propagation

推倒視頻連接

數學證實
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
高手學習筆記

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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
大牛學習博客

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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