斯坦福机器学习课程原始讲义cs229-notes.pdf,CS229 Lecture notes Andrew Ng 1 The perceptron and large margin classifiers In this final set of notes on learning theory, we will introduce a different m of machine learning. Specifically, we have so far been co
还以为失忆了 notes 1里面介绍3种方法 1.gradient descent (梯度下降) a.batch gradient descent b.stochastic gradient descent (上面的变形) 2.the normal equations 3.Newton method(Fisher scoring) 1.gradient descent algorithm α is called the learning rate. 仅仅有一个训练数据的样例: 这个式子直观上也...
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,”
斯坦福CS229机器学习笔记-Lecture9- Learning Theory 学习理论 声明:此系列博文根据斯坦福CS229课程,吴恩达主讲所写,为本人自学笔记,写成博客出来博文中部分图片和公式都来源于CS229官方notes。CS229的视频和讲义均为互联网公开资源...事件(但不一定互相独立),则有: 就是说,k个事件同时发生的概率 最多是 k个不同...
斯坦福机器学习-cs229-notes2.pdf,CS229 Lecture notes Andrew Ng Part IV Generative Learning algorithms So far, we’ve mainly been talking about learning algorithms that model p (y |x; θ), the conditional distribution of y given x. For instance, logistic r
课程链接: STATS214 / CS229M: Machine Learning Theory时间顺序记录lecture notes中的阅览进度 Chapter 1 Supervised Learning Formulations
cs229斯坦福大学机器学习教程 Supplemental notes 4 - hoeffding
machine learning in practice 我总认为计算机科学动手实践非常重要,纸上谈兵不接地气。coursera有programing exercise,必须完毕下。octave用起来挺爽的。 这里记录一下关键点。 1.coursera的cost function多除了一个m 事实上起到一个归一化的作用,让迭代步长α与训练样本数无关(你能够当作α=α'/m) ...
课程机器学习斯坦福andrew ng教授cs229 lecture10.pdf,MachineLearning-Lecture10 Instructor (Andrew Ng):So just a couple of quick announcements. One is, first, thanks again, for all of your Problem Set 1 submissions. They’ve all been graded, and we’ll retu
斯坦福CS229机器学习笔记-Lecture9- Learning Theory 学习理论 声明:此系列博文根据斯坦福CS229课程,吴恩达主讲 所写,为本人自学笔记,写成博客分享出来 博文中部分图片和公式都来源于CS229官方notes。 CS229... 机器学习学习笔记1(Ng课程cs229) 什么是机器学习 作为机器学习领域的先驱,Arthur Samuel在 IBM Journal of...