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CascadiaPrime - Deep Learning

Deep Learning is a branch of artificial intelligence (AI) / Machine Learning that has produced important gains in the last few years.

It's applications are most pronounced in voice recognition and vision processing and nearly everyone who uses Google, Microsoft or Apple products is the beneficiary.

This is a field that is developing very rapidly for there are immediate practical applications with immediate financial returns.

  ICLR 2015 Invited Talk: David Silver (Google DeepMind) "Deep Reinforcement Learning" ( May 2015)
  Deep Minds: An Interview with Google's Alex Graves & Koray Kavukcuoglu (Google DeepMind)(October 2015)
  Ruslan Salakhutdinov - Deep learning - Changing the playing field of artificial intelligence (CIFAR)
  Reinforcement Learning Course by David Silver Lecture 1: Introduction to Reinforcement Learning (May 2015)
  Quanta Magazine: As Machines Get Smarter, Evidence They Learn Like Us (July 2013)
  Stanford Deep Learning Tutorial Evidence (2014)
  Unsupervised Feature Learning and Deep Learning - Stanford Tutorial (April 2013)
  Andrew Ng - Machine Learning via Large-scale Brain Simulations (June 2013)
  Geoff Hinton - Deep Learning (June 2015)
  Geoff Hinton - Layman level interview about deep learning (May 2015)
  Geoff Hinton - What's wrong with convolutional nets? (December 2014)
  Geoff Hinton - "Dark knowledge" defines a similarity metric over the classes (November 2014)
  Geoff Hinton U of T Recent Developments in Deep Learning (June 2013)
  Geoff Hinton - Brains, Sex, and Machine Learning (August 2012)
  Geoff Hinton U of T, Dropout: A simple and effective way to improve neural networks (December 2012)
  Yann LeCun, NYU, Facebook, Interview (December 2014)
  Yann LeCun, NYU, Facebook, What's Wrong with Deep Learning (July 2015)
  Yann LeCun, NYU, Learning Representations: A Challenge for Learning Theory (June 2013)
  Yann LeCun: Deep Learning and the Representation of Natural Data - slides 2013
  Yosh Bengio U of Montreal Deep Learning of Representations (Dec 2012)
  Yosh Bengio - U of Montreal - Deep Learning of Representations
  (When there is only small data) How transferable are features in deep neural networks? (November 2014)
  ConvNetJS Deep Learning in your browser
  Marc'Aurelio Ranzato - Deep Learning for Vision: Tricks of the Trade w/history Oct 2013
  Nando de Freitas - UBC - Deep Learning
  Nando de Freitas - Toward the Implementation of a Quantum RBM
  Alex Smola - Intro to Deep Learning Sep 2013
  Deep Learning, Feature Learning - 2012 - IPAM Grade School - UCLA
  Caltech Machine Learning Course
  Deep Learning 101 and going forward issues
  Deep Learning Net
  Technion Panel: Is Deep Learning the Final Frontier? Critiques Jul 2014
  ECML - Text classification with Machine Learning Service
  Tushar Chandra: DSN 2014 Keynote: "Sibyl: A System for Large Scale Machine Learning at Google"
  IBM: Cognitive Computing: The SyNAPSE Project - Overview Promo video
  IBM Research Website: Cognitive Computing
  Cognitive Computing Programming Paradigm: A Corelet Language for Composing Networks of Neurosynaptic Cores
  Dharmendra S Modha's Cognitive Computing Blog
  Probabilistic Programming for Advanced Machine Learning”(PPAML) - Future of Machine Learning
  Graphlab - Retrospective Lecture on progress
  Hack The Multiverse - D-Wave - Quantum Computing - Machine Learning
   D-Wave Critic - Scott Aaronson
  How To Create A Mind: Ray Kurzweil at TEDxSiliconAlley (2012)
  Papers: Advances in Neural Information Processing Systems 27 (NIPS 2014))
  Yulia Sandamirskaya describes active paradigm for audio/visual learning
  DeepLearning.University – An Annotated Deep Learning Bibliography
  Neil: Never Ending Image Learner
  FastML Machine learning made easy
  Vancouver Institute for Visual Analytics (VIVA)
  Canadian Network for Visual Analytics (CANVAc)
  See Also the Artificial Intelligence Section of Cascadiaprime
  See Also the Quantum Computing Revolution Section of Cascadiaprime