Machine Learning - IV. Linear Regression with Multiple Variables (Week 2)

http://blog.csdn.net/pipisorry/article/details/43529845

机器学习Machine Learning - Andrew NG courses学习笔记

multivariate linear regression多变量线性规划

(linear regression works with multiple variables or with multiple features)

Multiple Features(variables)多特征(变量)

技术分享

{x上标i代表第i个trainning example;  x下标i代表特定trainning example中的第i个数值}


the hypothesis for linear regression with multiple features(variables)多变量线性回归的假设函数的表示
技术分享


additional zero feature x0(为了方便表示)

for every example i have a feature vector X superscript I and X superscript I subscript 0 is going to be equal to 1.



Gradient Descent for Multiple Variables多变量的梯度下降

模型表示

技术分享

通过gradient descent algorithm求解cost func最小值来求parameters θ

技术分享

{其中左边是单变量线性规划求解参数的gradient descent algorithm;

右边是多变量线性规划求解参数的算法}


Gradient Descent in Practice I - Feature Scaling梯度下降实践1 - 特征缩放




Gradient Descent in Practice II - Learning Rate梯度下降实践2 - 学习率




Features and Polynomial Regression特征和多项式回归



Normal Equation普通方程






from:http://blog.csdn.net/pipisorry/article/details/43529845


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