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YouTube (2010) Recent Developments in Deep Learning (1hr) TUTORIALS ....Tutorial (2009) Deep Belief Nets (3hrs) ppt pdf readings....Workshop Talk (2007) How to do backpropagation in a brain (20mins) ppt2007 pdf2007 ppt2014 pdf2014 : 2012 COURSERA COURSE LECTURES: Neural Networks for Machine Learning ....Lectures(.mp4) ....Lecture Slides(.pptx ...
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YouTube (2010) Recent Developments in Deep Learning (1hr)...
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Hinton, G. E., Osindero, S. and Teh, Y. (2006) A fast...
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AT&T Bell Labs (2 day), 1988Apple (1 day), 1990Digital Equipment Corporation (2 day), 1990Government of Canada (2 day), 1994A two-day intensive Tutorial on Advanced Learning Methods. Presented by Geoffrey Hinton and Michael Jordan Boston (Dec 1996); Los Angeles (Apr 1997); Washington (May 1997)Gatsby Computational Neuroscience Unit, University College London 1999 (4.5 hours)University College London, July 2009 (3 hours)Cambridge Machine Learning Summer School, September 2009 (3 hours)Technology Transfer Institute. 3 day tutorials presented in LosAngeles, Washington and San Francisco in 1988Ontario Information Technology Research Center (2 day), annually from 1988-1997American Association for Artificial Intelligence (half-day),1987, 1988, 1990International Joint Conference on Neural Networks (1 hour), 1990Neural Information Processing Systems Conference (2 hours), 1995Neural Information Processing Systems Conference (2 hours), 2007People also ask
Did Geoffrey Hinton lead to deep learning?
How did Geoffrey Hinton train deep belief networks?
Who wrote learning internal representations by error propagation?
[Coursera] Neural Networks for Machine Learning — Geoffrey Hinton. Colin Reckons. Learn about artificial neural networks and how they're being used for machine learning, as applied to speech...
![Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7 "Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]")
Lecture 1.3 — Some simple models of neurons [Neural Networks for Machine Learning]44.5K Views- Lecture 1.1 — Why do we need machine learning [Neural Networks for Machine Learning]209.8K Views
- Lecture 2.1 — Types of neural network architectures [Neural Networks for Machine Learning]44.1K Views
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- Lecture 2.3 — A geometrical view of perceptrons [Neural Networks for Machine Learning]24.1K Views
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Mar 21, 2019 · Mar 21, 2019. 890 likes | 1.04k Views. NCAP Summer School 2010 Tutorial on: Deep Learning. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Overview of the tutorial. Download Presentation. learning. belief nets. hidden units. joint configuration. directed acyclic graph.
Hinton, G. E., Learning Multiple Layers of Representation, Trends in Cognitive Sciences, Vol. 11, (2007) pp 428-434. Hinton G.E., Tutorial on Deep Belief Networks, Machine Learning Summer School, Cambridge, 2009 Andrej Karpathy, Li Fei-Fei. Deep Visual-Semantic Alignments for Generating Image Descriptions. CVPR 2015.
Geoffrey Hinton * The replicated feature approach (currently the dominant approach for neural networks) Use many different copies of the same feature detector with different positions. Could also replicate across scale and orientation (tricky and expensive) Replication greatly reduces the number of free parameters to be learned.
