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They result in significant space savings with negligible performance degradation.These functionalities are available in the Tensorly package with MXNet backend interface for large-scale efficient learning.
Katleen Gabriels: To realize that these algorithms are neither neutral nor value-free is an essential starting point.Algorithms already decide on our love life on dating apps and dating websites, our potential jobs (as companies can use them to scan our resumes), and even in court cases.Or consider for instance ‘recommender engines’ such as Google’s search engine: numerous people worldwide inform themselves daily about the world on a platform where algorithms decide which information you will or will not see.And the company keeps the algorithms themselves secret.Unfortunately, still too many people think that the ranking of the results is based on ‘reliability’.It could be something as simple as a run away script or learning how to better use E-utilities, for more efficient work such that your work does not impact the ability of other researchers to also use our site.
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Large-scale Machine Learning: Deep, Distributed and Multi-Dimensional: Modern machine learning involves deep neural network architectures which yields state-of-art performance on multiple domains such as computer vision, natural language processing and speech recognition.
As the data and models scale, it becomes necessary to have multiple processing units for both training and inference.
Our first ML Conference will debut in December in Berlin.
Until then, we’d like to give you a taste of what’s to come. Katleen Gabriels, Assistant Professor at Eindhoven University of Technology about how algorithms influence our daily lives and why ethics are essential to the development of machine learning.
She has been featured in a number of forums such as the yourstory, Quora ML session, O’Reilly media, and so on. Tech in Electrical Engineering from IIT Madras in 2004 and her Ph D from Cornell University in 2009.