machine learning trends from nips14

Machine Learning for Developers Most developers these days have heard of machine learning, but when trying to find an 'easy' way into this technique, most people find themselves getting scared off by the abstractness of the concept of Machine Learnin

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been

MachineLearning 欢迎任何人参与和完善:一个人可以走的很快,但是一群人却可以走的更远 Machine Learning in Action (机器学习实战) | ApacheCN(apache中文网) 视频每周更新:如果你觉得有价值,请帮忙点 Star[后续组织学习活动:sklearn + tensorflow] ApacheCN - 学习机器学习群[629470233] 第一部分 分类 1.) 机器学习基础 2.) k-近邻算法 3.) 决策树 4.) 基于概率论的分类方法:朴素

本文主要记录<Machine Learning In Action>中第二章的内容.书中以两个具体实例来介绍kNN(k nearest neighbors),分别是: 约会对象预测 手写数字识别 通过“约会对象”功能,基本能够了解到kNN算法的工作原理.“手写数字识别”与“约会对象预测”使用完全一样的算法代码,仅仅是数据集有变化. 约会对象预测 1 约会对象预测功能需求 主人公“张三”喜欢结交新朋友.“系统A”上面注册了很多类似于“张三”的用户,大家都想结交心朋友.“张三”最开始通过自己筛选的

本文汇编了一些机器学习领域的框架.库以及软件(按编程语言排序). 1. C++ 1.1 计算机视觉 CCV —基于C语言/提供缓存/核心的机器视觉库,新颖的机器视觉库 OpenCV—它提供C++, C, Python, Java 以及 MATLAB接口,并支持Windows, Linux, Android and Mac OS操作系统. 1.2 机器学习 MLPack DLib ecogg shark 2. Closure Closure Toolbox—Clojure语言库与工具的分类目录 3

http://blog.csdn.net/pipisorry/article/details/43973171 James Faghmous提醒机器学习初学者要避免的三方面错误,推荐阅读 New to Machine Learning? Avoid these three mistakes Common pitfalls when learning from data Machine learning (ML) is one of the hottest fields in data scien

http://blog.csdn.net/pipisorry/article/details/43884027 机器学习Machine Learning - Andrew NG courses学习笔记 Classification  0.1表示含义 denote with 0 is the negative class denote with 1 is the positive class.  Hypothesis Representation  Decision Boundary  Cost

博客已经迁移至Marcovaldo's blog (http://marcovaldong.github.io/) 刚刚完毕了Cousera上Machine Learning的最后一周课程.这周介绍了machine learning的一个应用:photo OCR(optimal character recognition,光学字符识别),以下将笔记整理在以下. Photo OCR Problem Description and Pipeline 最后几小节介绍机器学习的一个应用--photo O

Titanic Data Science Solutions¶ https://www.kaggle.com/startupsci/titanic-data-science-solutions 数据挖掘竞赛七个步骤:¶ Question or problem definition. Acquire training and testing data. Wrangle, prepare, cleanse the data. Analyze, identify patterns, and explo