The Ruby Reflector

…and ggplot2 I produced a plot (similar to the one presented in the Machine Learning for Hackers book) to show the results of the classifier.

Our results were captured in a CSV file and looked a little like this:

A,0.25,0.15 A,0.2,0.19 B,0.19,0.25

Each line contains the item's actual class, the predicted probability for membership of class A, and the predicted probability for membership of class B. Using ggplot2 we produce the following:

Items have been classified …

• Topics: Forward, n., Clojure, Bayes, Learning
• the evolving ultrasaurus: getting started with r
• Nimble Code: Waves of Annotation
• Engine Yard Developer Blog: Data Science at Engine Yard
• Signal vs. Noise: The three secrets of business analytics (no rocket science here)
• Global Nerdy: Tufte's "Data Analysis for Politics and Policy" Available Online for Free
• occasionally useful: ReReplay: Replay production traffic

…Abstractions for Structured Computation .

You may remember Prismatic from previous profile we did on HighScalability: Prismatic Architecture - Using Machine Learning On Social Networks To Figure Out What You Should Read On The Web . We learned how Prismatic, an interest driven content suggestion service, builds programs in terms of graph stuff:

• Topics: Prismatic, Architecture, Social network, Read
• The Curiosity Project: How to Use Prismatic to Discover the News You Care About

In update to Prismatic Architecture - Using Machine Learning on Social Networks to Figure Out What You Should Read on the Web , Jason Wolfe, even in the face of deadening fatigue from long nights spent getting their iPhone app out, has gallantly agreed to talk a little more about Primatic's approach to Machine Learning.

Documents and users are two areas where Prismatic applies ML (machine learning):

ML on Documents

• Topics: ML, Jason, Prismatic, Social network, Architecture
• Intridea - Company Blog: RubyWorld Conf 2010
• igvita.com: Intuition & Data-Driven Machine Learning
• pingles: Visualising classifier results with R and ggplot2
• SD Ruby Podcast: Episode 079: Artificial Stupidity: Adding Smarts to Yer Kode
• Dalibor Nasevic: More technical writings
• RubyFlow - Search for jruby: Ruby Midwest - Speakers Announced!

Vim Tips - Part I (June 27, 2012)

Vim Tips - Part II (July 3, 2012)

Rails 3 upgrade process (the road from 2.3.14 to 3.2.6) (June 21, 2012)

Intro to Machine Learning in Ruby (December 30, 2011)

Remote pair-programming with Screen (November 3, 2011)

Redis in the NoSQL ecosystem (December 28, 2011)

• Topics: Tips, Deployment, NoSQL
• Derek Powazek: links for 2010-08-25
• giant robots smashing into other giant robots: The FactoryGirl Representation of a Factory
• Global Nerdy: The Road to MIX
• OnRails.org: 360Flex last day.
• Recent Posts in Announcements | Rails Magazine: Rails Magazine Issue #4 - Free Download
• ZURB: Podcast of Peter Skillman's talk on Innovating the Mobile Experience

…and ggplot2 I produced a plot (similar to the one presented in the Machine Learning for Hackers book) to show the results of the classifier.

Our results were captured in a CSV file and looked a little like this:

A,0.25,0.15 A,0.2,0.19 B,0.19,0.25

Each line contains the item's actual class, the predicted probability for membership of class A, and the predicted probability for membership of class B. Using ggplot2 we produce the following:

Items have been classified …

• Topics: Class, Alex, Clojure, Bayes, Y
• pingles: Evaluating classifier results with R part 2
• Nimble Code: Waves of Annotation
• occasionally useful: ReReplay: Replay production traffic
• Signal vs. Noise: What I do with data at 37signals
• Heroku: Clojure on Heroku

Practical Machine Learning in Python

From: NextDayVideo

Views: 4930

52 ratings Time: 29:35 More in Education

• Topics: NextDayVideo
• Videos related to 'Ruby on Rails vs Java - Commercial #1 of 9': 11. A Startup Junkie's Journey to Python via Java, C#, Ruby and Scala
• Videos related to 'Ruby on Rails vs PHP - Commercial #3 of 9': Introduction to Django
• SD Ruby Podcast: Episode 079: Artificial Stupidity: Adding Smarts to Yer Kode
• igvita.com: Intuition & Data-Driven Machine Learning
• RubyFlow - Search for jruby: Ruby Midwest - Speakers Announced!
• Engine Yard Developer Blog: March Madness!

Two days. One track. November 4-5 in Kansas City, MO. - $ 189

Keynotes

Uncle Bob Martin

Andy Hunt

Sessions

Jim Weirich - Mastering the Ruby Debugger

Matt Kirk - Recommendation Engines using Machine Learning, and JRuby

Angela Harms - Does pair programming have to suck?

Dave Copeland - Make Awesome Command-Line Apps with Ruby

Marty Haught - Ruby Community: Awesome; Could Be Awesomer

Zach Holman - How GitHub Uses GitHub to Build GitHub

• Topics: Ruby on Rails, Red Hat Linux, Dave Copeland, James Edward Gray II, Evented

GoGaRuCo 2010: Machine Learning Trends & Patterns

The goal for my presentation at GoGaRuCo 2010 ( slides ) was to highlight the following themes: first, the algorithm is important, but we tend to overemphasize its importance; simple, intuitive insights are usually the underpinnings of much more (seemingly) complicated algorithms; data can be an algorithm in by itself. None of these ideas are novel or my own. In fact, much of the recent credit goes to Peter Norvig for popularizing …

• Topics: Ilya Grigorik, Google, 2001, GoGaRuCo, Google Translate
• Engine Yard Developer Blog: March Madness!
• SD Ruby Podcast: Episode 079: Artificial Stupidity: Adding Smarts to Yer Kode
• Intridea - Company Blog: RubyWorld Conf 2010
• PaulBarry.com: How To Write A Spelling Corrector In Ruby
• Giles Bowkett: January AI/Machine Learning Wishlist

I attended the conference this year and presented Ruby and Machine Learning. The slides are available here . In my presentation which was attended by Matz and Chad Fowler, I did a brief introduction to Machine Learning( ML), talked about the current state of Ruby and Machine Learning, surveyed the ML tools available today, and demonstrated Ruby code that uses ML. I also talked about and demonstrated tweetsentiments.com - an application that I developed using Ruby on Rails…

• Topics: Ruby ecosystem, Ruby on Rails, Matsue, Japan, Yukihiro Matsumoto
• Practicing Ruby: Reflections from "The Compleat Rubyist"
• Programblings: Rubinius for the Layman, Part 3 - Try Rubinius in 20 minutes

Machine learning and data mining have long been "black arts" because they require access to expensive computing clusters. Randall Thomas discusses machine learning and the problem spaces it can help solve.

Bonus content: download the slides from this talk.

• Engine Yard Developer Blog: March Madness!
• igvita.com: Collaborative Filtering with Ensembles