Note:该笔记参考的是2012版的讲义,因为我是有一定的基础的,故此记录一些自己不会的或者是平时少了解的。 Part I Linear Regression 2 The normal equation 这里其实就是用矩阵的形式来计算least squares. Denotes: Training set: X:[m,n]; weights θ:[n,1] . 这里其实就说明了ML的一个大概念,学习每个...
EM算法,翻译过来就是期望最大化。我们很容易联想到最大似然法(Maximum likelihood),这两个算法肯定有联系与区别的,不着急,且听贫道慢慢道来~侠肝义胆陈浩天:概率(probability) 与似然(likelihood) 首先我们知…
Inthissetofnotes,wediscusstheEM(Expectation-ization)forden- sityestimation. Supposethatwearegivenatrainingset{x(1),...,x(m)}asusual.Since weareintheunsupervisedlearningsetting,thesepointsdonotcomewith anylabels. Wewishtomodelthedatabyspecifyingajointdistributionp(x(i),z(i))= p(x(i)|z(i))p...
the proof ofNormal equationand, before that, somelinear algebra equations, which will be used in the proof. The normal equation Linear algebra preparation For two matrices and such that is square, . Proof: Some properties: some facts of matrix derivative: Proof: Proof 1: Proof 2: Proof: (...
CS229 Lecture notes 1 supervised learningNg, Andrew
公开项目>cs229-notes5-Regularization cs229-notes5-Regularization Fork 0 喜欢 8 分享 pdf 小动物值日生 7枚 BML Codelab 2.4.0 Python3 计算机视觉 2023-07-01 23:28:41版本内容 Fork记录 评论(0) 运行一下 v1 2023-07-01 23:29:35 请选择预览文件 当前Notebook没有标题 BML Codelab基于JupyterLab...
公开项目>cs229-notes1-Supervised learning cs229-notes1-Supervised learning Fork 0 喜欢 2 分享 pdf讲义 小动物值日生 7枚 BML Codelab 2.4.0 Python3 机器学习 2023-06-30 14:21:43版本内容 Fork记录 评论(0) 运行一下 v1 2023-06-30 14:22:26 请选择预览文件 当前Notebook没有标题 BML Codelab...
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) ...
最近在学机器学习,用的是cs229,打算边学习边翻译,加深理解。 CS229-notes 监督式学习(supervised learning problems) Part I 线性回归(Linear Regression) 监督式学习(supervised learning problems) 让我们以一些监督式学习问题的例子来开始吧。假设我们有一个数据集,给出了俄勒冈州波特兰市47栋房屋的居住面积和价格:...
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 ...