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John Langford is a machine learning research scientist, a field which he says “is shifting from an academic discipline to an industrial tool”.

His primary research interest is in understanding how to get human knowledge into a machine learning system in the most efficient way possible.

He works primarily in the areas of language (computational linguistics and natural language processing) and machine learning (structured prediction, domain adaptation and Bayesian inference).

He associates himself most with conferences like ACL, ICML, NIPS and EMNLP, and has over 30 conference papers (one best paper award in ECML/PKDD 2010) and 7 journal papers.

Natural Language Processing: Crash Course Computer Science #36

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The 7 Steps of Machine Learning

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Natural Language Generation at Google Research

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Dr. Yann LeCun, "How Could Machines Learn as Efficiently as Animals and Humans?"

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Introduction to Natural Language Processing - Cambridge Data Science Bootcamp

Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: ...

Probabilistic Machine Learning in TensorFlow

In this episode of Coffee with a Googler, Laurence Moroney sits down with Josh Dillon. Josh works on TensorFlow, Google's open source library for numerical ...

Tensorflow, deep learning and modern RNN architectures, without a PhD by Martin Gorner

The hottest topic in computer science today is machine learning and deep neural networks. Many problems deemed "impossible" only 5 years ago have now ...

Deep Learning Approach for Extreme Multi-label Text Classification

Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels.

Three Cool Things About D - The Case for the D Programing Language

Google Tech Talk July 29, 2010 ABSTRACT C++ has been through many battles and won most of them. Invariably it has been patched with more armor, given ...

Machine Learning and Robust Optimization, Fengqi You, Cornell University

When Machine Learning Meets Robust Optimization: Data-driven Adaptive Robust Optimization Models, Algorithms & Applications In this presentation, we will ...