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NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Machine Learning...

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Machine Learning and Hadoop by Josh Wills Abstract: We'll review common use cases for machine learning and advanced analytics found in our customer base at Cloudera and ways in which Apache Hadoop supports these use cases. We'll then discuss upcoming developments for Apache Hadoop that will enable new classes of applications to be supported by the system.
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GoogleTechTalks
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Time:
40:48
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NIPS 2011 Music and Machine Learning Workshop: Learning from Musical Structure

International Music and Machine Learning Workshop: Learning from Musical Structure at NIPS 2011 Invited Talk: A Topic Model for Melodic Sequences by Athina Spiliopoulou Athina is a PhD student in the Machine Learning group of the Institute for Adaptive and Neural Computation at the School of Informatics,University of Edinburgh. She works with Amos Storkey on Machine Learning methods for music, with a specific interest in unsupervised learning of musical structure from melodic sequences.
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Time:
28:57
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NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: A Common GPU...

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: A Common GPU n-Dimensional Array for Python and C by Arnaud Bergeron Abstract: Currently there are multiple incompatible array/matrix/n-dimensional base object implementations for GPUs. This hinders the sharing of GPU code and causes duplicate development work.This paper proposes and presents a first version of a common GPU n-dimensional array(tensor) named GpuNdArray~\citep{GpuNdArray} that works with both CUDA and OpenCL.It will be usable from python, C and possibly other languages.
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GoogleTechTalks
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Time:
13:45
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NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Big Machine Learning...

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Big Machine Learning made Easy by Miguel Araujo Miguel Araujo holds a BS and MS in computer science from Universidad Antonio de Nebrija and San Diego State University. He is a Machine Learning addict and an active open source hacker that enjoys coding in Python. Miguel is a contributor in open source projects like: django-rules, django-crispy-forms, and requests-oauth. Abstract: While machine learning has made its way into certain industrial applications, there are many important real-world domains, especially domains with large-scale data, that remain unexplored. There are a number of reasons for this, and they occur at all places in the technology stack.
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GoogleTechTalks
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Time:
27:52
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