December 1, 2016 by Charlie
Video | Strata + Hadoop World NYC 2016 | “The Evolution of Massive Scale Data Processing”

In this video, Tyler Akida presents a whirlwind tour of the evolution of massive-scale data processing at Google, from the original MapReduce paradigm to the high-level pipelines of Flume to the streaming approach of MillWheel to the portable, unified streaming/batch model of Google Cloud Dataflow and Apache Beam (incubating).

Tyler also highlights similarities and differences with related open source systems such as Flink, Spark, Storm, and Gearpump, calling out ways in which they’re converging on and diverging from the Beam model and what that means when running Beam pipelines on their respective runners. Watch Video

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November 15, 2016 by Charlie
VIDEO | NYC Machine Learning Meetup 2016 | Dan Melamed “How To Learn From What Your Users Might Not See”

At the Machine Learning Meetup in NYC, Dan Melamed gave a machine learning talk titled: “How To Learn From What Your Users Might Not See”. This talk will focus on contextual bandits and their applications.

In this tutorial, Dan will show how to learn from such data in a principled, efficient, and unbiased manner. The techniques that he will describe were largely responsible for a click-thru rate gain of over 25% on MSN.com. Watch Video

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November 4, 2016 by Charlie
Edu-Videos | GOTO Copenhagen 2016 | Machine & Deep Learning

The 20th anniversary of the GOTO Copenhagen Conference took place last month. Two 50 Minute videos – Machine Learning and Deep Learning. READ MORE

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October 25, 2016 by Charlie
Scala World 2016 | Martin Odersky Keynote

Scala World 2016 took place in the UK in September – Here Martin Odersky, the creator of the Scala programming language gives the keynote. And link to other videos Read More

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October 17, 2016 by Ted O'Brien
“10 Myths About Machine Learning” – (Prof. Pedro Domingos)

Machine Learning is at the core of data science and we see it’s applications all over now (i.e. recommender engines, etc.). As Pedro Domingos’s Professor of Computer Science U. Washington writes in the piece, “In reality, the main purpose of machine learning is to predict the future.” It’s important to be aware of the MYTHs associated with Machine Learning. Read More

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Blog Publisher / Head of Data Science Search

Founder & Head of Data Science Search at Starbridge Partners, LLC.