“TRANSFER LEARNING” From The Harvard Business Review – using the recent Presidential Election & lack of data to illustrate Transfer Learning: “a field that helps to solve these problems by offering a set of algorithms that identify the areas of knowledge which are “transferable” to the target domain. This broader set of data can […]
VIDEOs From the DataEngConf NYC in November. “Demystifying Deep Learning with Visualizations” (44 min) AND “Python Data Wrangling: Preparing for the Future” (37 min).
(Reposted due to popular demand) Another great video from Josh Wills. Josh is Sr. Director of Data Science at Cloudera and has a gift for making fairly complicated technology explanations very digestible to the novice and intermediary techie.
What I most love about this video is how Josh explains -very clearly – the issue of translating analytics Machine Learning on a large set of data records (many individuals) and making it work well in a “real life” production environment on a single individual (think eCommerce). Watch Video
The University of Illinois’ Coordinated Science Laboratory had it student run conference last month. This video is Dr. Andy Feng – VP Architecture at Yahoo! He leads the architecture and design of big data and machine learning initiatives. “In this talk, we illustrate Yahoo use cases and datasets, and explain the evolution of big-data technology stack.” Watch Video
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
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
The 20th anniversary of the GOTO Copenhagen Conference took place last month. Two 50 Minute videos – Machine Learning and Deep Learning. READ MORE
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
Renee Teate Interviews Debbie Berebichez, Chief Data Scientist of Metis for the “Becoming A Data Scientist” series. Watch Interview
Video (33:41) with Databricks co-founder and CTO Matei Zaharia presenting the changes in Apache Spark 2.0 and the general availability (GA) of Databricks Community Edition at Spark Summit 2016. Afterwards, Michael Armbrust demos some new features found in Spark 2.0 on Databricks Community EditionGo To Video