'Geoffrey hinton' diaporamas de présentation

Social Computing and Big Data Analytics ??????????

Tamkang University. Tamkang University. Deep Learning with Theano and Keras in Python (Python Theano ? Keras ???? ). Social Computing and Big Data Analytics ??????????. 1042SCBDA09 MIS MBA (M2226) (8628) Wed, 8,9, (15:10-17:00) (B309). Min-Yuh Day ??? Assistant Professor ??????

786 views • 78 slides

Learning and Remapping in the Sensory Motor System

Learning and Remapping in the Sensory Motor System. F.A. Mussa-Ivaldi Northwestern University. In natural arm movements, complex motions of the joints lead to simple hand kinematics. From Morasso 1981. Is the shape of trajectories the byproduct of dynamic optimization?.

624 views • 43 slides

Restricted Boltzmann Machines and Deep Belief Networks

Restricted Boltzmann Machines and Deep Belief Networks. Presented by Matt Luciw USING A VAST, VAST MAJORITY OF SLIDES ORIGINALLY FROM: Geoffrey Hinton, Sue Becker, Yann Le Cun , Yoshua Bengio , Frank Wood . Motivations.

2.67k views • 38 slides

CSC 2515 2008 Lecture 10 Support Vector Machines

CSC 2515 2008 Lecture 10 Support Vector Machines. Geoffrey Hinton. Getting good generalization on big datasets. If we have a big data set that needs a complicated model, the full Bayesian framework is very computationally expensive.

486 views • 35 slides

CSC321: Introduction to Neural Networks and Machine Learning Lecture 15: Mixtures of Experts

CSC321: Introduction to Neural Networks and Machine Learning Lecture 15: Mixtures of Experts . Geoffrey Hinton. Very local models e.g. Nearest neighbors Very fast to fit Just store training cases Local smoothing obviously improves things. Fully global models e. g. Polynomial

571 views • 19 slides

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks

CSC321: 2011 Introduction to Neural Networks and Machine Learning Lecture 7: Learning in recurrent networks. Geoffrey Hinton. Recurrent networks. A feed-forward neural net just computes a fixed sequence of non-linear learned transformations to convert an input patter into an output pattern.

438 views • 17 slides

Deep Learning for Speech Recognition and Related Applications

1.07k views • 11 slides