文献导读 - Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

原文:http://googleresearch.blogspot.jp/2010/04/lessons-learned-developing-practical.html Lessons learned developing a practical large scale machine learning system Tuesday, April 06, 2010 Posted by Simon Tong, Google Research When faced with a hard pre

ON THE EVOLUTION OF MACHINE LEARNING: FROM LINEAR MODELS TO NEURAL NETWORKS We recently interviewed Reza Zadeh (@Reza_Zadeh). Reza is a Consulting Professor in the Institute for Computational and Mathematical Engineering at Stanford University and a

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

转自 http://scikit-learn.org/stable/tutorial/basic/tutorial.html#machine-learning-the-problem-setting In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than

from: http://www.erogol.com/broad-view-machine-learning-libraries/ http://www.slideshare.net/VincenzoLomonaco/deep-learning-libraries-and-rst-experiments-with-theano FEBRUARY 6, 2014 EREN 1 COMMENT Especially, with the advent of many different and in

课讲内容 这门课以8周设计,分成 4个核心问题,每个核心问题约需2周的时间来探讨.每个约2个小时的录影中,每个小时为一个主题,以会各分成4到5个小段落,每个段落里会有一个后多个随堂的练习.我们在探讨每个核心问题的第二周.依上所述,課程的規畫如下: When Can Machines Learn? [何时可以使用机器学习] 第一周:(NTU-Coursera机器学习:机器学习问题与二元分类) 第一讲:The Learning Problem [机器学习问题]第二讲:Learning to Answ

COMMON PITFALLS IN MACHINE LEARNING JANUARY 6, 2015 DN 3 COMMENTS Over the past few years I have worked on numerous different machine learning problems. Along the way I have fallen foul of many sometimes subtle and sometimes not so subtle pitfalls wh

Here is the note for lecture three. the linear model Linear model is a basic and important model in machine learning. 1. input representation The data we get usually needs some changes, most of them is the input data. In linear model, input =(x1,x2,x

发现自己不写总结真是件很恶劣的事情,好多学的东西没有自己总结都忘记了.所以决定从今天开始,学东西的时候一定跟上总结. 我写的东西大多数是自己通俗的总结,不太喜欢写严格的定义或者证明,写了也记不住,欢迎指正. 1. High Bias vs. High Variance High Bias:通常是因为模型过于简单,使得不能成功拟合数据.比如说一些有二次曲线特性的数据,如果用一次直线去拟合就会出现这个问题,所以它对应了Underfitting问题.另外,从泛化角度来说,这样的模型泛化程度更高. Hi