This is a friendly Lambda Calculus Introduction by Dustin Mulcahey. LISP has its syntactic roots in a formal system called the lambda calculus. After a brief discussion of formal systems and logic in general, Dustin will dive in to the lambda calculus and make enough constructions to convince you that it really is capable of expressing anything that is “computable”. Dustin then talks about the simply typed lambda calculus and the Curry-Howard-Lambek correspondence, which asserts that programs and mathematical proofs are “the same thing”. This talk was recorded at the Lisp NYC meetup at Meetup HQ.
Oct 282013
In this talk, , the founder of R-bloggers, will present his recent work “dendextend,” a package intended for visualizing and comparing trees of hierarchical clusterings (a.k.a: dendrograms) with R. This talk was recorded at the New York Statistical Programming meetup at Knewton.
Tal begins his presentation with a short overview of ”dendrogram” object in R and its manipulation with the “dendextend” package. He then discusses how to create, change, visualize, and statistically compare two trees of hierarchical clusterings (with some sprinkles of Rcpp).
Tal ends with a 5-minute lightening talk teaching how one can quickly update R on windows/mac, using the ‘installr’ package.
In this talk and Patrick Philips of LinkedIn will present how the LinkedIn data science team hacks data science using sophisticated data mining and crowdsourcing techniques to leverage the data they already have and create the data that’s missing. This talk was recorded at SF Data Mining meetup at Trulia.
Tom Pinckney of eBay discusses eBay’s new graph-based recommendation engine. The first part of the talk is spent on discussing the theory behind the recommendation engine and the conceptual pieces of how it works.
The second part of the talk is spent on discussing eBay’s implementation of Cassandra to implement the recommendation engine. In particular, the focus is on how eBay is able to use Cassandra to handle the tremendous scale that eBay needs for this kind of a recommendation system. As you can imagine, eBay has a tremendous amount of data that is constantly coming in. In using Cassandra, Tom explains how eBay is able to handle that load.
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Jun 252013
This presentation was from , a quantitative modeling engineer at Google. It was recorded at a SF data mining meetup. The talk focused on the role of Crowdsourcing in the data analysis toolkit. Specifically, Edwin discussed use cases, including measuring the relevance of personalized search, gathering training data for machine learning classifiers and designing a system that incorporates crowdsourcing in real-time, high-quality research.
He also talked about advantages and challenges of crowdsourcing using using Mechanical Turk, and examples of the level of quality that is possible from Workers.
This talk is by , Lead Engineer at Bitly, and , a Software Engineer at Bitly, recorded at the eBay NYC offices.
Matt and Jehiah will be talking about NSQ, their open sourced project that solves an issue of realtime distributed message processing, designed to operate at bitly’s scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee.
MongoNYC 2013 is coming in 2 days!
http://www.10gen.com/events/mongonyc-2013
MongoNYC brings together developers, IT professionals and executive decision makers across the MongoDB community for a one-day conference dedicated to the leading NoSQL database. At MongoNYC, you learn development and operations best practices, discover how other businesses are benefiting from MongoDB and network with MongoDB users and ecosystem partners.
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In this talk, we’ll see how recommendation systems are created from data. What’s the algorithm? What’s the evaluation method? What’s the optimization procedure? When does it converge? We’ll talk about parallelizing in order to scale up to “big data” size via the MapReduce framework. Finally, we’ll think about priors and how they are overloaded. Content from this talk draws from chapters in Doing Data Science contributed by David Crawshaw and Matt Gattis.
Podcast: Play in new window | Download
Bio, etc…
(Original post with video of talk here)
Ben Engber, CEO and founder of Thumbtack Technology, will discuss how to perform tuned benchmarking across a number of NoSQL databases. He describes a NoSQL Database Comparison across Couchbase, Aerospike, MongoDB, Cassandra, HBase and others in a way that does not artificially distort the data in favor of a particular database or storage paradigm. This includes hardware and software configurations, as well as ways of measuring to ensure repeatable results.
Ben: Hi, My name’s Ben Engber. I’m the founder of a company called Thumbtack Technology. We are a consulting company, with one of our primary practice areas being doing NoSQL development and advising clients on NoSQL. And, the background of this talk is, you know, one of the things that comes up really often when we talk to clients, one of the first things they ask us is, ‘What NoSQL database should we use?’ And then, you know, the followup is, ‘Well, we need to learn a little bit about your business, so let’s do some discovery’. It’s the correct answer, but it often doesn’t go over that well. So, what we wanted to do, is we wanted to have sort of at least a basic baseline which would introduce them to the main concepts to give them right off the bat, and then sort of introduce a deeper discussion based on that.
So, about six months ago, we started researching within our company to do some NoSQL database comparisons, and research on the subject. And, this presentation is sort of presents a way that we can perform NoSQL database comparison. So, in some ways, what I’m going to do is come in and argue with everything that Will just said about why you can’t build an abstraction layer.