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Machined Learnings

An old curse says: "may you always be assigned to a test bucket."

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Machined Learnings | machinedlearnings.com Reviews

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An old curse says: &quot;may you always be assigned to a test bucket.&quot;

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Machined Learnings: February 2014

http://www.machinedlearnings.com/2014_02_01_archive.html

Cue Butlerian Jihad in 3, 2, 1, . Friday, February 21, 2014. Stranger in a Strange Land. I attended the SIAM PP 2014. The Data Must Flow. One of the first things I heard at the conference was that “map-reduce ignores data locality”. The speaker, Steve Plimpton, clearly understood map-reduce, having implemented MapReduce for MPI. This was a big clue that they mean something very different by data locality (i.e., they do not mean “move the code to the data”). Moderately distorted, is a good match for what ...

2

Machined Learnings: October 2014

http://www.machinedlearnings.com/2014_10_01_archive.html

Cue Butlerian Jihad in 3, 2, 1, . Tuesday, October 21, 2014. A recently published piece by Isaac Asimov titled On Creativity. Partially rebuts my previous post. Here's a key excerpt:. To feel guilty because one has not earned one’s salary because one has not had a great idea is the surest way, it seems to me, of making it certain that no great idea will come in the next time either. I'm not entirely sure, but I'll make a pop culture analogy. I'm currently enjoying the series The Knick. Over time procedur...

3

Machined Learnings: March 2014

http://www.machinedlearnings.com/2014_03_01_archive.html

Cue Butlerian Jihad in 3, 2, 1, . Sunday, March 9, 2014. I spent Christmas break working on some matrix factorization related ideas. There were two things I was wondering about: first, whether dropout is a good regularizer for discriminative low-rank quadratics; and second, how to do the analog of GeV representation learning. Startups have popularized the idea of failing fast. Since most business ideas are also not good ideas. The idea of the minimum viable product. It should work allowed me to power thr...

4

Machined Learnings: Wages and the Immigration Debate

http://www.machinedlearnings.com/2015/02/wages-and-immigration-debate.html

Cue Butlerian Jihad in 3, 2, 1, . Saturday, February 28, 2015. Wages and the Immigration Debate. It's a reasonable question. According to the BLS. One possibility is that compensation has gone up, but they aren't measuring correctly. That table says “total compensation”, which the footnote says “Includes wages, salaries, and employer costs for employee benefits.” So I suspect (hope! This explanation still leaves open the question: “why don't employers just skip all that stuff, have dumpy offices wi...

5

Machined Learnings: Bears Talking about Machine Learning and Immigration Policy

http://www.machinedlearnings.com/2011/10/bears-talking-about-machine-learning.html

Cue Butlerian Jihad in 3, 2, 1, . Thursday, October 13, 2011. Bears Talking about Machine Learning and Immigration Policy. Inspired by a Forbes article. About US immigration policy reform for skilled workers, I decided to make this video. Enjoy! Also, if you understand the machine learning and the optimization, feel free to contact me about employment. The State of the Machine Learning Labor Market. Update: I moved this to github. Because xtranormal went out of business. Posted by Paul Mineiro.

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Inherent Uncertainty: Online Dating + Learning

http://www.inherentuncertainty.org/2010/12/online-dating-learning.html

A range of thoughts on Learning and Game-playing, Prediction and Decision-making. Dec 14, 2010. Posted by Jake Abernethy. I just returned from NIPS 2010. The final year the conference will be held in Vancouver, and I think this was my best experience at the conference so far. But maybe I'm just happy to be done with the Hyatt for a few years. While in Vancouver, I had an excellent chat with Paul Mineiro (he has a nice blog. Who works for eHarmony. Who works at okCupid. That "22 percent of heterosexual co...

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Data | DataCricket

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US States Postal Abbreviations. Leave a Reply Cancel reply. Your email address will not be published. Required fields are marked *. Blog by Tim Manns (data mining blog). Burak Kanber's Blog. Data Mining: Text Mining, Visualization and Social Media. Hi I'm Hilary Mason. Large Scale Machine Learning and Other Animals. Statistical Modeling, Causal Inference, and Social Science. Yet Another Blog in Statistical Computing. Magazine Basic created by c.bavota.

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DataCricket | Page 317 of 490 |

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Map of book subjects on Internet Archive. October 20, 2014. The Internet Archive makes millions of digitized books available in the form of scanned pages, and these books are categorized …. Read more →. By Rob J Hyndman. October 20, 2014. Read more →. Estimating a Beta Regression with The Variable Dispersion in R. October 19, 2014. Read more →. Interesting dataset from Ancestry.com. October 19, 2014. Read more →. October 19, 2014. Read more →. Your Paper Makes SSRN Top Ten List. October 19, 2014. I recei...

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Cathy O'Neil, mathbabe, Author at DataCricket

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Author Archive for Cathy O'Neil, mathbabe. Chicago’s Heat List predicts arrests, doesn’t protect people or deter crime. By Cathy O'Neil, mathbabe. August 18, 2016. Read more →. By Cathy O'Neil, mathbabe. August 16, 2016. Read more →. The truth about clean swimming pools. By Cathy O'Neil, mathbabe. August 12, 2016. There’s been a lot of complaints about the Olympic pools turning green and dirty in Rio. People seem worried that the swimmers’ health may be at risk and so on. Well, here&r...Read more →.

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Justin Kestelyn (@kestelyn), Author at DataCricket

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Author Archive for Justin Kestelyn (@kestelyn). How-to: Index Scanned PDFs at Scale Using Fewer Than 50 Lines of Code. By Justin Kestelyn (@kestelyn). October 19, 2015. Read more →. Untangling Apache Hadoop YARN, Part 2. By Justin Kestelyn (@kestelyn). October 16, 2015. Read more →. How-to: Use Apache Solr to Query Indexed Data for Analytics. By Justin Kestelyn (@kestelyn). October 14, 2015. Bet you didn’t know this: In some cases, Solr offers lightning-fast response times for business-style querie...

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Andrew, Author at DataCricket

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Author Archive for Andrew. Bayesian inference completely solves the multiple comparisons problem. August 22, 2016. I promised I wouldn’t do any new blogging until January but I’m here at this conference and someone asked me a question about the above slide from my talk. The point of the story in that slide is that flat priors consistently give bad inferences. Or, to put it another way, the routine use of […]. The post Bayesian inference completely solves the multiple comparisons problem. Read more →.

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Nathan Yau, Author at DataCricket

http://www.datacricket.com/author/nathan-yau

Author Archive for Nathan Yau. Colorgorical generates color schemes for you. August 22, 2016. Sometimes you need a color scheme quick, and ColorBrewer is typically the de facto, but it has some limitations. You can’t just choose any color you want as a starting…. Read more →. The different trends in American crime. August 19, 2016. It depends on who you ask and where. The Marshall Project analyzed violent crime trends over the past 40 years to show how things…. Read more →. All the National Food Days.

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Links - FastML

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Machine learning made easy. A couple of pages we occasionaly read:. GitHub meets Arxiv, or machine learning papers with code. Heavy focus on deep learning. An Armenian blog on neural networks. Delip Rao recently started blogging, looks good so far. Https:/ jmetzen.github.io. Jan Hendrik Metzen has a blog on machine learning and python. We posit that you’re gonna like it. Http:/ www.inference.vc. Ferenc Huszar explains some deep learning papers he liked. Https:/ jakevdp.github.io/. Paul Mineiro, at. It an...

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The StatMT Blog: Welcome to the StatMT Blog

http://statmt.blogspot.com/2007/08/welcome-to-statmt-blog.html

A blog for and about statistical machine translation. Thursday, August 30, 2007. Welcome to the StatMT Blog. Welcome to the StatMT blog, a blog for the statistical machine translation community! Given the success of other research blogs, such as Hal Daume's NLP blog. This blog should be primarily be forum for the discussion of ideas. We anticipate offering posts on the following topics:. Negative results that might not otherwise be published. MT in the media. We'll do our best to keep this interesting.

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eleg.ant.ly: September 2009

http://eleg.ant.ly/2009_09_01_archive.html

Hardy's cafe de code. Thursday, September 24, 2009. Roll your own cloud based backup. So I'm moving out of my current apartment shortly, ahead of giving a talk at Hadoop World NYC. And going on a two week vacation overseas. I'm going to be putting my stuff in storage while I'm away, and this includes my hard drives with my photos and music on them. It occurred to me, well what if this gets stolen, or the drives damaged in transit? It was time to make a backup. Long overdue, really. AWS) Note that this st...

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Art Engineers vs. Machine Divas. An ongoing Research and Networking Project by Andrea Sodomka and Eva Ursprung. In May 2007, Andrea Sodomka and Eva Ursprung started their investigation on Machine Divas – woman artists using technology for their work in nowadays expanded theater space . The first results were presented at the IMA Salon #7 07 at ars electronica and put forward for discussion on a panel with Inke Arns, Kathy Rae Huffman and Eva Wohlgemuth.

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New Year's Eve @ Palace Hotel. 35 Hanover Street, Charleston SC. Spinning new and classic House,. Latin House and Hip Hop. Lazers - Drink Specials -. Windjammer on Isle of Palms. Friday, January 09, 2015. Nikki Haley Pre-Inaugural Family Fun Night. Tuesday, January 13, 2015. SC State Museum, Columbia 6-8pm. Sunday, January 25, 2015. Follow Machine on SoundCloud. For the latest tracks and performance sets.

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Machined Learnings

An old curse says: "may you always be assigned to a test bucket.". Tuesday, March 6, 2018. Pytorch PU learning trick. I'm often using positive-unlabeled learning. Nowadays. In particular for observational dialog modeling, next utterance classification. Structure your model to have a leftforward and a rightforward like this:. At training time, compute the leftforward and rightforward for your mini-batch distinctly:. Criterion = BatchPULoss() model = MyModel() . leftvec = model.leftforward(batch&#4...Impor...

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