machine learning(10) -- classification:logistic regression cost function

logistic regression cost function

图像分布

时间： 2024-10-14 00:45:26

machine learning(10) -- classification:logistic regression cost function的相关文章

CheeseZH: Stanford University: Machine Learning Ex3: Multiclass Logistic Regression and Neural Network Prediction

Handwritten digits recognition (0-9) Multi-class Logistic Regression 1. Vectorizing Logistic Regression (1) Vectorizing the cost function (2) Vectorizing the gradient (3) Vectorizing the regularized cost function (4) Vectorizing the regularized gradi

Machine Learning Techniques -5-Kernel Logistic Regression

5-Kernel Logistic Regression Last class, we learnt about soft margin and its application. Now, a new idea comes to us, could we apply the kernel trick to our old frirend logistic regression? Firstly, let's review those four concepts of margin handlin

CheeseZH: Stanford University: Machine Learning Ex5:Regularized Linear Regression and Bias v.s. Variance

源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin

Machine learning with python - Linear Regression

Machine learning with python Linear Regression 数据来自 cs229 Problem Set 1 (pdf) Data: q1x.dat, q1y.dat, q2x.dat, q2y.dat PS1 Solution (pdf) 从左上往右下 batchGradientDescent的cost随迭代次数的增加而下降,和收敛结果 stochasticGradientDescent的cost随迭代次数的增加而下降,和收敛结果 normalEquatio

Andrew Ng Machine Learning 专题【Linear Regression】

此文是斯坦福大学,机器学习界 superstar - Andrew Ng 所开设的 Coursera 课程:Machine Learning 的课程笔记. 力求简洁,仅代表本人观点,不足之处希望大家探讨. 课程网址:https://www.coursera.org/learn/machine-learning/home/welcome Week 3: Logistic Regression & Regularization 笔记:http://blog.csdn.net/ironyoung/ar

Deep learning：四(logistic regression练习)

前言: 本节来练习下logistic regression相关内容,参考的资料为网页:http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=DeepLearning&doc=exercises/ex4/ex4.html.这里给出的训练样本的特征为80个学生的两门功课的分数,样本值为对应的同学是否允许被上大学,如果是允许的话则用'1'表示,否则不允许就用'0'表示,这是一个典型的二分类问题.在此问题中,给出的80个

machine learning(11) -- classification: multi-class classification

Multiclass classification例子: 邮箱的邮件的分类: 工作邮件,私人邮件,朋友的邮件,兴趣爱好的邮件医学诊断: 没有生病,患有流感,患有普通感冒天气: 晴天,兩,多云等 One-vs-all classfication = one-vs-rest : 每一次将一个class分出来,共构建3个classifiers hθ(i)(x) = P(y=i|x;θ) (i=1;2;3) train a logistic regression classifier hθ(i

转载 Deep learning：四(logistic regression练习)

machine learning(11) -- classification: advanced optimization

其它的比gradient descent快, 在某些场合得到广泛应用的求cost function的最小值的方法 Conjugate gradient, BFGS,L-BFGS很复杂,可以在不明白详细原理的情况下进行应用. 可以使用Octave和matlab的函数库直接进行应用,这些软件里面的build-in libarary已经很好的实现了这些算法. 当要使用其它的语言来实现这些算法时,如c,c++,Java等,要多试几种实现,因为不同的实现方法在性能上相差很大.