NIUHE
日々私たちが过ごしている日常というのは、実は奇迹の连続なのかもしれんな
正则化解决过度拟合问题
前言：通常一个学习演算法是借由训练范例来训练的。亦即预期结果的范例是可知的。而学习者则被认为须达到可以预测出其它范例的正确的结果，因此，应适用于一般化的情况而非只是训练时所使用的现有资料（根据它的归纳偏向）。然而，学习者却会去适应训练资料中太特化但又随机的特征，特别是在当学习过程太久或范例太少时。在过适的过程中，当预测训练范例结果的表现增加时，应用在未知资料的表现则变更差。——From WikiPedia
Logistic Regression
Now we are switching from regression problems to classification problems. Don't be confused by the name "Logistic Regression"; it is named that way for historical reasons and is actually an approach to classification problems, not regression problems.
Octave Tutorial
Linear Regression with Multiple Variables
Multiple Features
Linear regression with multiple variables is also known as "multivariate linear regression".
We now introduce notation for equations where we can have any number of input variables.
Linear Regression with One Variable
Model Representation
Recall that in regression problems, we are taking input variables and trying to map the output onto a continuous expected result function.
Linear regression with one variable is also known as "univariate linear regression."
Univariate linear regression is used when you want to predict a single output value from a single input value. We're doing supervised learning here, so that means we already have an idea what the input/output cause and effect should be.
Introduction to machine learning
What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."
TCP/IP 浅析
海量数据挖掘（三）：Finding Similar Sets
此系列为Cousera上Standford的Mining Massive Datasets课程学习笔记。 这是该系列的第三篇笔记：Finding Similar Sets