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Computational Thinking

Computational Thinking

What does it mean to think like a computer? What does it mean to think with a computer? Explore the possibilities for improving your development skills.

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Business | Computational Thinking
E145/146
Tutorial Please note: to attend, your registration must include Tutorials.
Paco Nathan (Liber 118)
Average rating: ***..
(3.20, 20 ratings)
Advanced math for business people: “just enough math” to take advantage of new classes of open source frameworks. Many take college math up to calculus, but never learn how to approach sparse matrices, complex graphs, or supply chain optimizations. This tutorial ties these pieces together into a conceptual whole, with use cases and simple Python code, as a new approach to computational thinking. Read more.
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Computational Thinking | Java & JVM
Portland 251
Tutorial Please note: to attend, your registration must include Tutorials.
Jason Swartz (Netflix, Inc), Kelsey Gilmore-Innis (Reverb)
Average rating: ****.
(4.30, 10 ratings)
Scala powers some of the biggest companies in the world, including Twitter, Intel, and LinkedIn. Come learn what led them to choose this powerful JVM language and try it out yourself. You’ll get a hands-on intro to Scala and functional programming concepts by building your own performant REST API. No FP experience needed--if you can build apps in Java, Python or Ruby you’ll do great in this class. Read more.
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Katie Miller (Red Hat)
Average rating: ***..
(3.44, 9 ratings)
For the uninitiated, a conversation with functional programmers can feel like ground zero of a jargon explosion. In this talk Lambda Ladies Co-Founder Katie Miller will help you to defend against the blah-blah blast by demystifying several terms commonly used by FP fans with bite-sized Haskell examples and friendly pictures. Expect appearances by Curry, Lens, and the infamous M-word, among others. Read more.
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Computational Thinking
Portland 255
Neal Ford (ThoughtWorks)
Average rating: ****.
(4.68, 19 ratings)
Learning the syntax of a new language is easy, but learning to think under a different paradigm is hard. This session helps you transition from a Java writing imperative programmer to a functional programmer, using Java, Clojure and Scala for examples. Read more.
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Computational Thinking
Portland 255
Garrett Smith (CloudBees)
Average rating: ****.
(4.12, 8 ratings)
Erlang is famous for building systems that are incredibly reliable, having virtually no down time! What are the principles that Erlang uses? Can we apply them in other languages? In this presentation, you'll learn how Erlang's design enables reliability and how you can use similar patterns to improve your own software and software systems. Read more.
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Computational Thinking
Portland 255
Tim Berglund (DataStax)
Average rating: ***..
(3.77, 30 ratings)
A brief and friendly tour of the basics of graph theory, including a description and classification of the kinds of graphs and some interesting problems they can be employed to solve. Read more.
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Computational Thinking
Portland 255
Ethan Dereszynski (Webtrends), Eric Butler (Cedexis)
Average rating: ***..
(3.40, 5 ratings)
Visitors to an online store rarely make their intention explicit. A valuable goal in digital marketing is to infer this intention so to influence the visitor's behavior in-situ. We describe a data-driven approach to identifying and predicting online user behavior. The talk focuses on the construction of real-time machine learning tools for inference to sites with thousands of concurrent visitors. Read more.
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Computational Thinking
Portland 255
William Cox (self)
Average rating: ***..
(3.57, 7 ratings)
In this talk we'll explore the Fourier transform and FIR filters in an intuitive way to make it accessible. You'll come out with the ability to look at your time-series data in a new way and explore new uses for otherwise useless data. Read more.
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Computational Thinking
Portland 255
Wynn Netherland (GitHub)
Average rating: ****.
(4.00, 9 ratings)
How do you build and maintain a stable API while rapidly iterating and innovating in your business? Change can never be eliminated, but its impact can be minimized. GitHub takes a pragmatic approach to Hypermedia that emphasizes workflows over data retrieval and employs open source to ensure a consistent experience for API consumers. Read more.
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Computational Thinking
Portland 255
Paco Nathan (Liber 118)
Average rating: ****.
(4.40, 5 ratings)
Several frameworks have emerged for handling data workflows. Meanwhile, business use of Machine Learning is less about algorithms and more about leveraging workflows. This talk compares/contrasts different workflow approaches with focus on use cases, plus how some leverage the PMML open standard. Summary points build a scorecard for evaluating frameworks based on your use case needs. Read more.
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Computational Thinking
Portland 255
Josh Patterson (Patterson Consulting), Adam Gibson (Skymind.io)
Average rating: ****.
(4.50, 2 ratings)
In this session, we will take a look at how we parallelize Deep Belief Networks in Deep Learning on the next​-generation YARN framework Iterative Reduce and the parallel machine learning library Metronome. We’ll also take a look at some real world applications of Deep Learning on Hadoop such as image classification and NLP. Read more.
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Computational Thinking
Portland Ballroom
Tim Berglund (DataStax)
Average rating: ****.
(4.40, 20 ratings)
A fun and approachable tour of some otherwise intimidating data structures. Learn how to solve difficult problems efficiently through the clever organization and linking of data. Read more.
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Rachael Madsen (Optimal Design Software LLC)
Multiple indexes into data structures add complexity and slow down processing. A single multi-keyed AVL tree can allow complex searches to be constructed more easily and performed quickly, with a single O(lg N) lookup. In this talk we will discuss how these trees work and how to implement them. Examples will be shown using python version 3, with C++ libraries for optimization of key routines. Read more.
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Computational Thinking
Portland 255
Kirsten Hunter (Akamai)
Average rating: ***..
(3.33, 6 ratings)
So you want to create a platform for your product? Creating a fantastic open API (or even a closed one) is not the same as creating other products. I'll talk about how what you need to know to design, plan and execute a successful, engaging API and how to avoid common pitfalls. Read more.
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Computational Thinking
Portland 255
Jeff Wolski (Uber)
Average rating: ***..
(3.00, 1 rating)
Uber is one of the fastest growing companies in the world and the real-time engineering team are responsible for their mission critical Node.js-powered systems. Learn how they are adapting their services to be autonomous, loosely-coupled and highly-available by applying the principles of event-driven architecture. Read more.
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Benjamin Curtis (Honeybadger Industries)
Average rating: ****.
(4.00, 3 ratings)
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. Read more.
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Computational Thinking
Portland 255
Francesco Cesarini (Erlang Solutions Ltd)
Average rating: ****.
(4.67, 3 ratings)
The actor model has received much attention because of its scalable and intuitive approach to concurrency. But the notion of concurrency is as fundamental to certain languages as object-orientation is to Java. In this talk, we will describe the evolution of concurrent thinking in Erlang, providing valuable lessons for Go, Rust, Elixir and AKKA developers who will undertake a similar journey.. Read more.
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Computational Thinking
Portland 255
Sudhir Tonse (Netflix), Danny Yuan (Netflix Inc)
Average rating: ****.
(4.50, 4 ratings)
This session presents the data platform used at Netflix for event collection, aggregation, and analysis. The platform helps Netflix process and analyze billions of events every day. Attendees will learn how to assemble their own large-scale data pipeline/analytics platform using open source software from NetflixOSS and others, such as Kafka, ElasticSearch, Druid from Metamarkets, and Hive. Read more.
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Computational Thinking
Portland 255
Chris Richardson (Chris Richardson Consulting, Inc)
Average rating: *****
(5.00, 4 ratings)
Higher-order functions such as map(), flatmap(), filter() and reduce() have their origins in mathematics and ancient functional programming languages such as Lisp. But today they have become mainstream and are available in languages such as JavaScript, Scala and Java 8. Learn how to they can be used to simplify code in a variety of domains including collection processing, concurrency and big data. Read more.