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Dr. Dre - The Next Episode
Music video by Dr. Dre performing The Next Episode
with Paul Hunter
(C) 1999 Interscope Records (C) 2000 Aftermath/Interscope Records
Length: 210
Rating: 4.90 (8850 ratings)
Tags: Dr. Dre The Next Episode Hip Hop Aftermath Paul Hunter
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Next Ronaldinho
PLease spread the word. Favorite this video and comment this video, if you like
This kid is only 10. He's next Ronaldinho. He's on many team's transfer list. His name is Muhammed he plays Besiktas U13 team.
This secret video shows his great talent. He is only 10 but in this video he's playing against players at 13 ages. Enjoy it.
Muhammet Demirci is only 10 years old. This video shows his great talent plays against 14 years old mates. He is maybe next Ronaldinho. Many team wants to buy him (Barcelana and PSV) He is wonderkid. Watch video and see him on your eyes.
May be you let your favourite team to hear about this wonderkid:)
Length: 128
Rating: 4.40 (2880 ratings)
Tags: ronaldinho cool talent impressive ball nike football kid turkey muhammet demirci wonderkid
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The Last Shadow Puppets - 'Standing Next to Me'
'Standing Next To Me' - new single - 7th July 2008
The Last Shadow Puppets release their second single, 'Standing Next To Me' on Monday the 7th of July 2008. Taken from their recent number 1 album 'The Age Of The Understatement', 'Standing Next To Me' features Alex and Miles on duel vocals, injecting a sense of flair and drama into a 2 minute 18 second pop gem.
Length: 141
Rating: 4.90 (2088 ratings)
Tags: Alex Turner Arcade Fire Belle Sebastian Arctic Monkeys Age Of Understatement Last Shadow Puppets Standing Next To Me
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The Next Generation of Neural Networks
Google Tech Talks
November, 29 2007
In the 1980's, new learning algorithms for neural networks promised to
solve difficult classification tasks, like speech or object recognition,
by learning many layers of non-linear features. The results were
disappointing for two reasons: There was never enough labeled data to
learn millions of complicated features and the learning was much too slow
in deep neural networks with many layers of features. These problems can
now be overcome by learning one layer of features at a time and by
changing the goal of learning. Instead of trying to predict the labels,
the learning algorithm tries to create a generative model that produces
data which looks just like the unlabeled training data. These new neural
networks outperform other machine learning methods when labeled data is
scarce but unlabeled data is plentiful. An application to very fast
document retrieval will be described.
Speaker: Geoffrey Hinton
Geoffrey Hinton received his BA in experimental psychology from Cambridge in
1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did
postdoctoral work at Sussex University and the University of California San
Diego and spent five years as a faculty member in the Computer Science
department at Carnegie-Mellon University. He then became a fellow of the
Canadian Institute for Advanced Research and moved to the Department of
Computer Science at the University of Toronto. He spent three years from 1998
until 2001 setting up the Gatsby Computational Neuroscience Unit at University
College London and then returned to the University of Toronto where he is a
University Professor. He holds a Canada Research Chair in Machine Learning. He
is the director of the program on "Neural Computation and Adaptive Perception"
which is funded by the Canadian Institute for Advanced Research.
Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada,
and the Association for the Advancement of Artificial Intelligence. He is an
honorary foreign member of the American Academy of Arts and Sciences, and a
former president of the Cognitive Science Society. He received an honorary
doctorate from the University of Edinburgh in 2001. He was awarded the first
David E. Rumelhart prize (2001), the IJCAI award for research excellence
(2005), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award
for contributions to information technology (1992).
A simple introduction to Geoffrey Hinton's research can be found in his
articles in Scientific American in September 1992 and October 1993. He
investigates ways of using neural networks for learning, memory, perception and
symbol processing and has over 200 publications in these areas. He was one of
the researchers who introduced the back-propagation algorithm that has been
widely used for practical applications. His other contributions to neural
network research include Boltzmann machines, distributed representations,
time-delay neural nets, mixtures of experts, Helmholtz machines and products of
experts. His current main interest is in unsupervised learning procedures
for neural networks with rich sensory input.
Length: 3563
Rating: 4.90 (171 ratings)
Tags: google techtalks techtalk engedu talk talks googletechtalks education
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