代码语言:txt 博文中部分图片和公式都来源于CS229官方notes。 代码语言:txt CS229的视频和讲义均为互联网公开资源 代码语言:txt Lecture4 Lecture4的主要内容: ·logistic regression 部分剩下的Newton’smethod ·Exponential family (指数分布族) Generalized linear model(广义线性模型GLM) 1、 Newton’s method 接...
CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes,we discuss the EM(Expectation-Maximization) for den-sity estimation. Suppose that we are given a training set {x(1) , . . . ,x(m)} as usual. Since we are in the unsupervised learning sett...
代码语言:txt **博文中部分图片和公式都来源于CS229官方notes。** 代码语言:txt 复制 **CS229的视频和讲义均为互联网公开资源** Lecture 2 这一节主要讲的是三个部分的内容: ·Linear Regression(线性回归) ·Gradient Descent(梯度下降) ·Normal Equations(正规方程组) 1、 线性回归 首先给了一个例子,如何...
CS229 Lecture notesAndrew NgMixtures of Gaussians and the EM algorithmIn this set of notes, we discuss the EM (Expectation-Maximization) for den-sity estimation.Suppose that we are given a training set {x(1),...,x(m)} as usual. Sincewe are in the unsupervised learning setting, these ...
斯坦福机器学习课程原始cs229-notes7b.pdf,CS229 Lecture notes Andrew Ng Mixtures of Gaussians and the EM algorithm In this set of notes, we discuss the EM (Expectation- ization) for den- sity estimation. Suppose that we are given a training set {x(1) , . .
内容提示: CS229 Lecture notesAndrew NgPart XFactor analysisWhen we have data x(i)∈ Rnthat comes from a mixture of several Gaussians,the EM algorithm can be applied to fit a mixture model. In this setting, weusually imagine problems where we have sufficient data to be able to discernthe...
CS229 Lecture notes 1 supervised learningNg, Andrew
斯坦福CS229机器学习笔记-Lecture4 - 指数分布族 和 广义线性模型 GLM,程序员大本营,技术文章内容聚合第一站。
机器学习教授作业及课程cs229 notes4.pdf,CS229 Lecture notes Andrew Ng Part VI Learning Theory 1 Bias/variance tradeoff When talking about linear regression, we discussed the problem of whether to fit a “simple” model such as the linear “y = θ +θ x,”
cs229 notes12强化学习与控制.pdf,CS229 Lecture notes Andrew Ng Part XIII Rein ment Learning and Control We now begin our study of rein ment learning an ptive control. In supervised learning, we saw algorithms that tried to make their outputs mimic the lab