A couple of years ago, a client asked me to build a recommendation engine for them. Coming into this with a minimal knowledge of statistical math, I ultimately built a relatively simple recommendation engine in Ruby. The design made heavy use of Redis Sets, Lists, and Hashes in order to greatly reduce the number of SQL queries to provide recommendations.
This talk will be a case study discussing the object-oriented considerations in designing a scalable service, how Redis was a good fit for the project, and some of the painful lessons that I learned along the way so that you don't have to repeat them.
Not sure where to cluster or where to classify? Have you seen a linear regression lately? Every wanted to take a look into machine learning? Curious to what problems you can solve? Using Ruby to become familiar with machine learning and data-mining techniques is great way to get acclimated before diving in with both feet.
Machine learning is everywhere these days. Features like search, voice recognition, recommendations - they’ve become so common that people have started to expect them. They’re starting to expect the apps we build to be smarter.
Ten years ago, machine learning and data mining techniques were only available to the people dedicated enough to dig through the math. Now that’s not the case.
The most common machine learning techniques are well known. Standard approaches have been developed. And, fortunately for us, many of these are available as ruby gems. Some are even easy to implement yourself.
In this presentation we’ll cover five important machine learning techniques that can be used in a wide range of applications. It will be a wide and shallow introduction, for Rubyists, not mathematicians - we’ll have plenty of simple code examples.
By the end of the presentation, you won’t be an expert, but you’ll know about a class of tools you may not have realized were available.
Let’s roll up our sleeves, and learn about Ruby and OpenCV. Let there be coding. Let there be learning. But most of all, let this be the most magical of all gatherings. I’ve put on my robe and my wizard hat, now lets make magic happen!
"That's stupidly awesome" or "You're such a jerk :)"
The above is obviously positive, but how would you train a computer to figure that out? So much of our language is contextual and has subtle hints of sentiment that this is a tough problem in natural language processing.
Though there is a great algorithm called Support Vector Machines that can find a close solution! And there's a great Ruby library for you to use as well.
Join us for this talk where we'll go detecting sentiment in tweets using support vector machines. At least join us for the various bouts of swear words and confusing lexicon of the English language.
From Amazon, to Spotify, to thermostats, recommendation systems are everywhere. The ability to provide recommendations for your users is becoming a crucial feature for modern applications. In this talk I'll show you how you can use Ruby to build recommendation systems for your users. You don't need a PhD to build a simple recommendation engine -- all you need is Ruby. Together we'll dive into the dark arts of machine learning and you'll discover that writing a basic recommendation engine is not as hard as you might have imagined. Using Ruby I'll teach you some of the common algorithms used in recommender systems, such as: Collaborative Filtering, K-Nearest Neighbor, and Pearson Correlation Coefficient. At the end of the talk you should be on your way to writing your own basic recommendation system in Ruby.
Would you like to do some OCR using Ruby and learn some machine-learning along the way?
In this talk, first up, we'll have a quick demo where an attendee writes down a digit on a sticky note and a Ruby program tries to to recognize it. Then we'll pick it apart, covering:
the basics of machine-learning (and different applications)
brief look into the Math behind supervised learning (classification)
(mostly) hand-rolled code applying this Math
libraries/tools available to Rubyists (and choice of Ruby platforms) including weka, mahout, libsvm, and the Ruby Matrix class :)
ways to dive deeper into ML
Neural networks (NNs) not only sound really cool, but they can also solve some pretty interesting problems ranging from driving cars to spam detection to facial recognition.
Solving problems with NNs is challenging, because actually implementing a NN from scratch is difficult, and knowing how to apply it is more difficult. Fortunately, libraries, such as RubyFANN, exist to handle the first problem. Solving the second problem comes from experience.
This talk will show a few different approaches to applying NNs to such problems as spam detection and games, as well as discussing other areas where NNs might be a useful solution.
Neural networks are an excellent way of mapping past observations to a functional model. Many researchers have been able to build tools to recognize handwriting, or even jaundice detection. While Neural Networks are powerful they still are somewhat of a mystery to many. This talk aims to explain neural networks in a test driven way. We’ll write tests first and go through how to build a neural network to determine what language a sentence is. By the end of this talk you’ll know how to build neural networks with tests!
Your Rails app is full of data that can (and should!) be turned into useful information with some simple machine learning technqiues. We'll look at basic techniques that are both immediately applicable and the foundation for more advanced analysis -- starting with your Users table.
We will cover the basics of assigning users to categories, segmenting users by behavior, and simple recommendation algorithms. Come as a Rails dev, leave a data scientist.