Abhishek Thakur,很多kaggler对他都非常熟悉,2017年,他在 Linkedin 发表了一篇名为Approaching (Almost) Any Machine Learning Problem的文章,介绍他建立的一个自动的机器学习框架,几乎可以解决任何机器学习问题,这篇文章曾火遍 Kaggle。 Abhishek在Kaggle上的成就: Competitions Grandmaster(17枚金牌,世界排名第3) ...
解决几乎任何机器学习问题(完整翻译) 英文原文:Approaching (Almost) Any Machine Learning Problem Kaggle团队| 07.21.2016 Kaggle大师Abhishek Thakur最初在2016年7月18日在这里发表了这篇文章。 一个数据科学家每天处理大量的数据。有人说,超过60-70%的时间花在了数据清理,数据转移和数据采集上,使得机器学习模型可以...
GitHub - abhishekkrthakur/approachingalmost: Approaching (Almost) Any Machine Learning Problemgithub.com/abhishekkrthakur/approachingalmost/tree/master 目录 一、环境配置 Kirito:Approaching (Almost) Any Machine Learning Problem2 赞同 · 0 评论文章 二、有监督和无监督学习 三、交叉验证 四、验证指标 五...
原文链接:approaching almost any machine learning problem--abhishek thakur 前言 几乎每一个科学家日常之一是处理大量的数据。有人说几乎是60-70%的时间是花在数据清洗中,以及将数据转变为适合机器学习的格式。这篇文章主要集中在于第二部分,例如:运用机器学习以及包括前面的步骤。这里所讨论的内容是基于我参加几百场...
These 10 examples give a good sense of what a machine learning problem looks like. There is a corpus of historic examples, there is a decision that needs to be modelled and a business or domain benefit to having that decision modelled and efficaciously made automatically. Some of these probl...
CS229 Machine Learning作业代码:Problem Set 1 牛顿法求解二分类逻辑回归参数 Repeat{ θ:=θ−H−1∇θl(θ)θ:=θ−H−1∇θl(θ) } 其中,Hessian矩阵H∈R(n+1)×(n+1)H∈R(n+1)×(n+1) (H)i,j=∂2J∂θi∂θj(H)i,j=∂2J∂θi∂θj 公式推导 代码 logistic...
1、Learning theory的研究 第一个是主动学习(active learning),关于主动学习的样本复杂度理论分析方面的工作比较多。也是learning theory学者喜欢的一个研究方向。因为主动学习相比传统的被动学习方法,在理论上存在着有效降低样本标签复杂度的可能性的。 第...
the problem is that we introduce an additional hyperparameter (gamma) that needs to be tuned. Also, this “kernel trick” does not work for any dataset, and there are also many more manifold learning techniques that are “more powerful”/appropriate than kernel PCA. For example, locally linea...
1. Understand the business problem first, then frame it as a Machine Learning problem. When you follow an online course or participate in a Kaggle competition, you do not need to define the ML problem to solve. You are told what to solve for (e.g., predict house prices) and how to ...
1.3 The Learning Problem- Applications of Machine Leanring 1.Daily Needs: Food, Clothing, Housing, Transportation(日常需求:食品,服装,住房,交通) 下面介绍一下机器学习在哪些领域可能会有所运用.因此挑了四个比较常见的运用场景:食品,服装,住房,交通. ...