整理自Adrew Ng 的 machine learning课程week3 目录: 二分类问题 模型表示 decision boundary 损失函数 多分类问题 过拟合问题和正则化 什么是过拟合 如何解决过拟合 正则化方法 1.二分类问题 什么是二分类问题? 垃圾邮件 / 非垃圾邮件? 诈骗网站 / 非诈骗网站? 恶性肿瘤 / 非恶性肿瘤? 用表达式来表示:$y\in\left \{ 0,1 \right \}$, \begin{Bmatrix} 0& : & nagetive &…

1. Sigmoid Function In Logisttic Regression, the hypothesis is defined as: where function g is the sigmoid function. The sigmoid function is defined as: 2.Cost function and gradient The cost function in logistic regression is: the gradient of the cos…

Machine Learning/Introducing Logistic Function 打算写点关于Machine Learning的东西, 正好也在cnBlogs上新开了这个博客, 也就更新在这里吧. 这里主要想讨论的是统计学习, 涵盖SVM, Linear Regression等经典的学习方法. 而最近流行的基于神经网略的学习方法并不在讨论范围之内. 不过以后有时间我会以Deep Learning为label新开一个系列, 大概写写我的理解. 总之Machine Learning的la…

Machine Learning Lab1 打算把Andrew Ng教授的#Machine Learning#相关的6个实验一一实现了贴出来- 预计时间长度战线会拉的比較长(毕竟JOS的7级浮屠还没搞定.) ----------------------------------------------------------------------------------------------------------------------------------- 实验内容: 线性拟合 实验材…

(1) How to comput the Cost function in Univirate/Multivariate Linear Regression; (2) How to comput the Batch Gradient Descent function in Univirate/Multivariate Linear Regression; (3) How to scale features by mean value and standard deviation; (4) Ho…

machine learning- linear regression with one variable(2) Linear regression with one variable = univariate linear regression: 由一个输入变量预测出一个output (regression problem预测连续的值).                                  single input<--->single output training set:…

Question 1 Consider the problem of predicting how well a student does in her second year of college/university, given how well they did in their first year. Specifically, let x be equal to the number of "A" grades (including A-. A and A+ grades)…

1.Multiple features So what the form of the hypothesis should be ? For convenience, define x0=1 At this time, the parameter in the model is a ( + 1)-dimensional vector, and any training instance is also a ( + 1)-dimensional vector. The dimension of t…

1.Model representation Our Training Set [训练集]: We will start with this ''Housing price prediction'' example first of fitting linear functions, and we will build on this to eventually have more complex models 2.Cost function 代价函数(平方误差函数):It figures ou…

1.Classification However, 2.Hypothesis Representation Python code: import numpy as np def sigmoid(z): return 1 / (1 + np.exp(-z)) ℎ () = ( = 1|; ) ℎ () = 0.7,表示有 70%的 几率为正向类,相应地为负向类的几率为 1-0.7=0.3 3.Decision Boundary We can use very complex models to…