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01:04:10

Practical Machine Learning

Posted by Seth Juarez  on  Oct 11, 2014

Seth Juarez introduces the nuML machine learning library, addressing the clustering issue in .NET applications by focusing on recommendation engines and anomaly detection.

43:56

Machine Learning at Netflix Scale

Posted by Aish Fenton  on  Oct 07, 2014 4

Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.

52:04

Enterprise IT: What's Beyond Virtualization

Posted by Derek Collison  on  Sep 14, 2014

Derek Collison discusses some of the technologies and approaches for building a self-healing infrastructure: Intelligent layer 7 SDN with semantic awareness, self healing techniques, etc.

43:05

Design Patterns for Large-Scale Real-Time Learning

Posted by Sean Owen  on  Apr 15, 2014

Sean Owen provides examples of operational analytics projects, presenting a reference architecture and algorithm design choices for a successful implementation based on his experience Oryx/Cloudera.

40:29

Creative Machines

Posted by Joseph Wilk  on  Feb 10, 2014

Joseph Wilk addresses the questions if machines can be creative and what's the place of artists in such a world?

44:45

From The Lab To The Factory: Building A Production Machine Learning Infrastructure

Posted by Josh Wills  on  Jan 16, 2014

Josh Wills discusses using Hadoop technologies to build real-time data analysis models with a focus on strategies for data integration, large-scale machine learning, and experimentation.

41:14

Machine Learning & Recommender Systems at Netflix Scale

Posted by Xavier Amatriain  on  Jan 16, 2014

Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.

38:46

Machine Learning for Relevance and Serendipity

Posted by Jenny Finkel  on  Oct 30, 2013

Jenny Finkel showcases Prismatic's use of machine learning and language processing to provide targeted content to their users based on a model built on users' way of interacting with their website.

Reverend Bayes, Meet Countess Lovelace: Machine Learning and Programming

Posted by Andy Gordon  on  Jan 22, 2013 1

Andy Gordon discusses machine learning using functional programming, explaining how Infer.NET Fun turns the succinct syntax of F# into an executable modeling language for Bayesian machine learning.

Machine Learning: A Love Story

Posted by Hilary Mason  on  Nov 09, 2010 16

Hilary Mason presents the history of machine learning covering the most significant developments in the area, and showing how bit.ly uses it to discover various statistical information about users.

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