无监督学习(Unsupervised Learning)可以分为两种: 化繁为简 聚类(Clustering) 降维(Dimension Reduction) 无中生有(Generation) 所谓的"化繁为简"的意思:现在有很多种不同的input,比如说:你现在找一个function,它可以input看起来很多像树的东西,output都是抽象的树,把本来比较复杂的input变成比较简单的output.那在做unsupervised learning的时候,你只会有function的其…

一 Unsupervised Learning 把Unsupervised Learning分为两大类: 化繁为简:有很多种input,进行抽象化处理,只有input没有output 无中生有:随机给一个input,自动画一张图,只有output没有input 二 Clustering 有一大堆image ,把他们分为几大类,给他们贴上标签,将不同的image用相同的 cluster表示. 也面临一个问题,要有多少种cluster呢? 有两种clustering的方法: 2.1 K-means(…

1    Unsupervised Learning 1.1    k-means clustering algorithm 1.1.1    算法思想 1.1.2    k-means的不足之处 1.1.3    如何选择K值 1.1.4    Spark MLlib 实现 k-means 算法 1.2    Mixture of Gaussians and the EM algorithm 1.3    The EM Algorithm 1.4    Principal Components…

Beginners Guide To Learn Dimension Reduction Techniques Introduction Brevity is the soul of wit This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. Intrigued ? Allow me to prove it using a s…

机器学习 Machine Learning:提供数据分析的能力,机器学习是大数据时代必不可少的核心技术,道理很简单:收集.存储.传输.管理大数据的目的,是为了“利用”大数据,而如果没有机器学习技术分析数据,则“利用”就无从谈起. 数据挖掘 Data mining:数据挖掘是从海量数据中发掘只是,这就比然涉及对海量数据的管理和分析.大体来说,数据库领域的研究为数据挖掘提供数据管理技术,而机器学习和统计学的研究为数据挖掘提供数据分析技术. 统计学:由于统计学的研究成果通常需要经由机器学习研究来形成有…

无监督学习(Unsupervised Learning) 聚类无监督学习 特点 只给出了样本, 但是没有提供标签 通过无监督学习算法给出的样本分成几个族(cluster), 分出来的类别不是我们自己规定的, 而是无监督学习算法自己计算出来的 K-means 聚类算法 规定 \(c^{(i)}\): 表示\(x^{(i)}\)属于哪个cluster, 如\(x^{(1)}\)属于\(c^{(1)}\)簇, 如果\(c^{(1)}=1\), 则\(x^{(1)}\)划分在第1个类别 \(\mu_k\…

Unsupervised Learning: Use Cases Contents Visualization K-Means Clustering Transfer Learning K-Nearest Neighbors The features learned by deep neural networks can be used for the purposes of classification, clustering and regression. Neural nets are s…

Unsupervised learning refers to data science approaches that involve learning without a prior knowledge about the classification of sample data. In Wikipedia, unsupervised learning has been described as "the task of inferring a function to describe h…

Supervised Learning In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized…

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. We can derive this structure by clustering t…