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A Visual Introduction to Machine Learning

A Visual Introduction to Machine Learning
Finding better boundaries Let's revisit the 73-m elevation boundary proposed previously to see how we can improve upon our intuition. Clearly, this requires a different perspective. By transforming our visualization into a histogram, we can better see how frequently homes appear at each elevation. While the highest home in New York is 73m, the majority of them seem to have far lower elevations. Your first fork A decision tree uses if-then statements to define patterns in data. For example, if a home's elevation is above some number, then the home is probably in San Francisco. In machine learning, these statements are called forks, and they split the data into two branches based on some value. That value between the branches is called a split point. Tradeoffs Picking a split point has tradeoffs. Look at that large slice of green in the left pie chart, those are all the San Francisco homes that are misclassified. The best split Recursion

Related:  Machine Learning - M2M - AIgummibearehausenmedical imagingGood practicesDATA VISUALIZATION

Rogue wave ahead Sailing history is rife with tales of monster-sized rogue waves — huge, towering walls of water that seemingly rise up from nothing to dwarf, then deluge, vessel and crew. Rogue waves can measure eight times higher than the surrounding seas and can strike in otherwise calm waters, with virtually no warning. Now a prediction tool developed by MIT engineers may give sailors a 2-3 minute warning of an incoming rogue wave, providing them with enough time to shut down essential operations on a ship or offshore platform. The tool, in the form of an algorithm, sifts through data from surrounding waves to spot clusters of waves that may develop into a rogue wave.

Python Map Reduce on Hadoop - A Beginners Tutorial November 17 2013 Share Tweet Post This article originally accompanied my tutorial session at the Big Data Madison Meetup, November 2013. The goal of this article is to: GPI: Graphical Programming Interface The Philips raw data reader node, for MR data, is now available as a binary release. Users of the Philips raw data formats can directly import the raw data into GPI and start investigating. Thanks to the tenacious efforts of Ryan Robison, the reader node supports a plethora of file formats such as .data, .list, .lab, .raw, .sin, .par, .xml, .rec, and .cpx at many different release levels. The package releases are available for download on GitHub. The ReadPhilips node parses the file contents for MR data, converts the data to a numpy numeric array and makes it available as an output port.

A Neural Network Playground Data Which dataset do you want to use? Features Which properties do you want to feed in? Click anywhere to edit. Weight/Bias is 0.2. Where Bars Outnumber Grocery Stores Back in 2008, the Floatingsheep group collected data about the number of bars across the United States, and they compared those counts against the number of grocery stores. Their map showed what they called the “beer belly of America”, which is a much higher than average number of bars in the Wisconsin area. I came back to the map recently, and three questions came to mind:

Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced — Humanizing Technology Tool: 31 Resources to Learn AI & Deep Learning, From Beginner to Advanced I’ve spent the last few weeks learning, well, about deep learning. I’ve parsed through the internet, read a ton, and tried to get a sense of where we are as a community. I wanted to put together a resource for someone who’s interested in getting up to speed as quickly as possible with the best of the best resources that I can find, from doing research, looking for funding, and learning about the various open-source frameworks to following along with excellent tutorials.

Finding the natural number of topics for Latent Dirichlet Allocation - Christopher Grainger Update (July 13, 2014): I’ve been informed that I should be looking at hierarchical topic models (see Blei’s papers here and here). Thanks to Reddit users /u/GratefulTony and /u/EdwardRaff for bringing this to my attention. However, Redditor /u/NOTWorthless says HDPs do not provide a ‘posterior on the correct number of topics in any meaningful sense’. I’ll do more research and do a follow-up post. You can follow the conversation on Reddit here.

Create the first plugin (XCode3) In the end of this tutorial you will know how to: Create a new OsiriX plugin templateCompile itInstall it in OsiriX This article is designed for users with XCode3, if you are using XCode4 please go to this article. Step 1: Locate the “Plugin Generator”. It comes with the osirix plugin source code. If you followed my tutorial “Setting Up Development Environment” we downloaded it.

Seeing Theory Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University's Royce Fellowship Program. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations. Statistics is quickly becoming the most important and multi-disciplinary field of mathematics. According to the American Statistical Association, "statistician" is one of the top ten fastest-growing occupations and statistics is one of the fastest-growing bachelor degrees.

SITU Studio Art Project Shows the Gulf of Inequity in New York Property Tax Assessments New York City calculates property taxes for condos and co-ops using a byzantine formula mandated by the state. Which is all well and good, except that the formula underestimates the value of New York’s most valuable properties—some of the most valuable residences on Earth—meaning that the billionaires who own them pay a fraction of the property taxes that other New Yorkers pay. Billionaires don’t pay property taxes in New York. While the property levy is New York City’s biggest source of revenue, it’s also inherently inefficient, assigning higher real property taxes to apartment-renters than to the universally maligned absentee foreign owners living in sky-gems along Billionaires’ Row. The state’s formula deprives New York of millions in revenue each year; it calls into question why former New York Mayor Michael Bloomberg was so eager to see billionaires move to New York in the first place if they weren’t ever going to be charged a dime in property (or income) taxes.

Where Computers Defeat Humans, and Where They Can’t Photo ALPHAGO, the artificial intelligence system built by the Google subsidiary DeepMind, has just defeated the human champion, Lee Se-dol, four games to one in the tournament of the strategy game of Go. Why does this matter? After all, computers surpassed humans in chess in 1997, when IBM’s Deep Blue beat Garry Kasparov. So why is AlphaGo’s victory significant? Arun et al measure with NPR data · GitHub Skip to content Learn more Please note that GitHub no longer supports old versions of Firefox.

Related:  Machine Learning