Category Archives: 精品分享

【MLT】12-Neural Network

本章是神经网络的入门,包含如下四个方面:

  1. Motivation:基本单位-神经元,与生物神经元的联系
  2. Neural Network Hypothesis:神经元的转换函数(激活函数),物理解释
  3. Neural Network Learning:网络权重的学习,网络权重的偏微分推导,反向传播(BP)
  4. Optimization and Regularization:网络优化,模型VC维,两种regularization方法(正则化项,early stopping)
 

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Support Vector Machines vs Artificial Neural Networks

svms.org网站上一篇神经网络和支持向量机的对比文章,这里做简单翻译,如有错误请在下方评论处留言,感谢作者!感谢爱可可-爱生活分享!

 

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【MLF】2-4-Non-Separable Data

Lecture 2: Learning to Answer Yes/No——Non-Separable Data

本节重点介绍了线性不可分数据的处理办法——PLA算法的改进算法——口袋算法(Pocket Algorithm)。

  • Content
  • Slide #1. More about PLA
  • Slide #2. Learning with Noisy Data
  • Slide #3. Line with Noise Tolerance
  • Slide #4. Pocket Algorithm
  • Slide #5. Fun Time
  • Slide #6. Summary

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【MLF】2-3-Guarantee of PLA

Lecture 2: Learning to Answer Yes/No——Guarantee of PLA

Slide #1. Linear Separability

我们来看看PLA(Perceptron Learning Algorithm)什么时候会停下来,在想这个问题之前不妨想想PLA的终止条件是什么,是PLA找到一条线能毫无错误地将数据样本分开,但是这有一个前提条件,就是数据样本可以用一条线分开,否则PLA永远无法将数据样本分开。

QQ截图20150606183755

 

 

 

 

 

 

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【MLF】2-2-Perceptron Learning Algorithm (PLA)

 Lecture 2: Learning to Answer Yes/No——Perceptron Learning Algorithm (PLA)

通过上节课我们知道了一个可能的Hypothesis长相(可能是一条线,或者是在空间里所有可能的高维平面),那么我们现在的问题是——我们要怎样设计一个算法从众多的线或者面里选择一条最好的出来。

不妨我们想想,我们认为最好的线是什么,理想上的f对吧?如果能算出理想的f,那么我们就没什么好做的了,因为我们不知道f,所以我们希望我们的g和f越接近越好。但我们知道我们的f是由我们的数据产生的。  Continue reading

【MLF】2-1-Perception Hypothesis Set

 Lecture 2: Learning to Answer Yes/No——Perception Hypothesis Set

本节是第二讲(Lecture 2: Learning to Answer Yes/No)的第一节课,主要是对感知器(本课第一个演算法)的入门。首先我们回顾了一下上一讲(Lecture 1: The Learning Problem),同样是通过信用卡检测的问题引入本节,机器学习演算法如何做判断是非(Learning to Answer Yes/No)的问题。

Slide #1. Roadmap

  • When Can Machines Learning?
  • ——Lecture 1: The Learning Problem
  • ——A takes D and H to get g
  • ————Course Introductionfoundation oriented and story-like
  • ————What is Machine Learninguse data to approximate target
  • ————Application of Machine Learningalmost everywhere
  • ————Components of Machine Learning: A takes D and H to get g
  • ————Machine Learning and Other Fieldrelated to DM, AI and Stats
  • ——Lecture 2: Learning to Answer Yes/No
  • ————Perception Hypothesis Set
  • ————Perception Learning Algorithm (PLA)
  • ————Guarantee of PLA
  • ————Non-Separable Data
  • ②Why Can Machines Learn?
  • ③How Can Machines Learn?
  • ④How Can Machines Learn Better?

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【MLF】1-5-Machine Learning and Other Fields

Lecture 1: The Learning Problem——Machine Learning and Other Fields

本节主要讲了ML和DM(二者难以区分)ML和AI(ML是实现AI的方法之一),以及ML和Stats(Stats是实现ML的方法之一)的关系。最后对本章(第一章)进行了总结。

  • difficult to distinguish ML and DM in reality
  • ML is one possible route to realize AI
  • statistics: many useful tools for ML

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【MLF】1-4-Components of Machine Learning

Lecture 1: The Learning Problem——Components of Machine Learning

本节课从一个例子(信用卡检测)说起,分别介绍了机器学习中的五个重要(unknown target function, training examples, learning algorithm, hypothesis set, final hypothesis)的部分(如下图)。machine learning: use data to compute hypothesis g that approximates target f。

Slide #1. Components of Learning: Metaphr Using Credit Approval

Application Information

  • age: 23 years
  • gender: female
  • annual salary: NTD 1,000,000
  • year in residence: 1 year
  • year in job: 0.5 year
  • current debt: 200,000

unknown pattern to be learned: ‘approve credit card good for bank’ Continue reading

【MLF】1-3-Applications of Machine Learning

Lecture 1: The Learning Problem——Applications of Machine Learning

本节课主要谈了下ML可以应用的领域(除衣食住行外,还有教育,娱乐等等)。

Slide #1. Daily Needs: Food, Clothing, Housing, Transportation

data ——》ML——》skill

  1. Food (Sadilek et al., 2013):①data: Twitter data (words + location)②skill: tell food poisoning likeliness of restaurant properly
  2. Clothing (Abu-Mostafa, 2012):①data: sales figures + client surveys②skill: give good fashion recommodations to clients
  3. Housing (Tsanas and Xifara, 2012):①data:characteristics of buildings and their energy load②skill: predict energy load of other buildings closely
  4. Transportation (Stallkamp et al,. 2012):①data: some traffic sign images and meanings②skill: recognize traffic signs accurately

ML is everywhere! Continue reading