Chirs Burges,微软的机器学习大神,Yahoo 2010 Learning to Rank Challenge第一名得主,排序模型方面有RankNet,LambdaRank,LambdaMART,尤其以LambdaMART最为突出,代表论文为:From RankNet to LambdaRank to LambdaMART: An Overview此外,Burges还有很多有名的代表作,比如:A Tutorial on Support Vector Machines for Pattern R...
《Machine Learning and Probabilistic Graphical Models Course》 介绍:Buffalo大学教授Sargur Srihari的“机器学习和概率图模型”的视频课程 《Understanding Machine Learning: From Theory to Algorithms》 介绍:耶路撒冷希伯来大学教授Shai Shalev-Shwartz和滑铁卢大学教授Shai Ben-David的新书Understanding Machine Learning: F...
reddit这个网站大家可能不太熟悉,但是它已经全美流量排名第四,仅次于Google,YouTube和Facebook,上面内容质量很高,非常专注,下面这个地址是机器学习的subreddit:https://www.reddit.com/r/MachineLearning/。 第一个月:数学 线性代数 看Gillbert Strang教授的教程足够了:https://www.youtube.com/playlist?list=PL49CF3...
《Machine Learning Course 180’》 介绍: 一个讲机器学习的Youtube视频教程。160集。系统程度跟书可比拟。 《回归(regression)、梯度下降(gradient descent)》 介绍: 机器学习中的数学,作者的研究方向是机器学习,并行计算如果你还想了解一点其他的可以看看他博客的其他文章 《美团推荐算法实践》 介绍: 美团推荐算法...
This document attempts to develop a curated list of Machine Learning resources, including books, papers, software, libraries, notebooks, etc. Most of the libraries are for Python though the rest of the materials here are generally suited for working with data. ...
reddit这个网站大家可能不太熟悉,但是它已经全美流量排名第四,仅次于Google,YouTube和Facebook,上面内容质量很高,非常专注,下面这个地址是机器学习的subreddit:https://www.reddit.com/r/MachineLearning/ 第一个月:数学 线性代数: 看Gillbert Strang教授的教程足够了:https://www.youtube.com/playlist?list=PL49CF37...
If a developer wants to “do” machine learning, should they really have to go and spend a bunch of years and tens or hundreds of thousands of dollars to get the requisite math and higher degrees? The answer is of course not! There is a better way. ...
“In the course of developing Infer.NET, we’ve also had to develop new ways to automate parts of the machine-learning process that were previously done by hand. But there are still a lot of unsolved problems in machine learning, and researchers are constantly making progress on these. “...
Key thing to realize here is that GANs essentially are learning the loss function for you – which is really one big step closer to toward the ideal that we're shooting for in machine learning. And of course you generally get much better results when you get the machine to learn something...
This is not to say, of course, that a well-trained machine learning system’s behavior must inherently be erratic to a detrimental degree. Rather, it should be understood and considered within the design of machine-learning-enhanced systems that their capacity for dealing with extraordinarily ...