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| Next Ronaldinho |
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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:)
Tags : ronaldinho cool talent impressive ball nike football kid turkey muhammet demirci wonderkid |
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Affichage : 3346186
Durée : 128 s |
| The Next Generation of Neural Networks |
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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.
Tags : google techtalks techtalk engedu talk talks googletechtalks education |
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Affichage : 41946
Durée : 3563 s |
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