/learning-intelligence-naturallanguageprocessing

various readings and papers on artifical learning and intelligence

artificial-learning-and-intelligence-

various readings and papers on artifical learning and intelligence . Deep Learning is a part of Machine learning which is used for many approaches to AI (not all) [1]

[1] http://www.deeplearningbook.org/contents/intro.html

readings (with video links etc.)

Initial Readings 0: Artificial Neural Networks
  1. http://karpathy.github.io/neuralnets/
  2. https://ujjwalkarn.me/2016/08/09/quick-intro-neural-networks/
  3. https://takinginitiative.wordpress.com/2008/04/03/basic-neural-network-tutorial-theory/
Initial Readings 1 : Deep Learning
  1. http://www.vision.jhu.edu/tutorials/ICCV15-Tutorial-Math-Deep-Learning-Intro-Rene-Joan.pdf
  2. http://cs229.stanford.edu/materials/CS229-DeepLearning.pdf
Initial Readings 2 : Convolutional Neural Networks
  1. https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
  2. https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/

-> Paper with application : End to End Learning for Self-Driving Cars :https://arxiv.org/abs/1604.07316

Initial Readings 3 : Reinforcement Learning
  1. http://karpathy.github.io/2016/05/31/rl/
  2. https://deepmind.com/blog/deep-reinforcement-learning/
  3. https://www.youtube.com/watch?v=RvgYvHyT15E

-> Paper with application : Playing Atari with Deep Reinforcement Learning https://arxiv.org/pdf/1312.5602v1.pdf

Initial Readings 4 : Generative Adversarial Networks
  1. Main Paper : https://arxiv.org/abs/1406.2661
  2. https://www.youtube.com/watch?v=AJVyzd0rqdc
  3. https://www.youtube.com/watch?v=RvgYvHyT15E
Initial Readings 5: Ladder Networks
  1. http://rinuboney.github.io/2016/01/19/ladder-network.html
  2. Main Paper: https://arxiv.org/abs/1507.02672
  3. https://www.youtube.com/watch?v=ZlyqNiPFu2s
  4. Deconstructing the Ladder Network Architecture : http://proceedings.mlr.press/v48/pezeshki16.pdf

Books

  1. A Course in Machine Learning : http://ciml.info/ By Hal Daumé
  2. Deep Learning : http://www.deeplearningbook.org/ By Ian Goodfellow and Yoshua Bengio and Aaron Courville
  3. Algorithms for RL : https://sites.ualberta.ca/~szepesva/RLBook.html By Csaba Szepesvári
  4. Reinforcement Learning: An Introduction , Second edition in progress , online draft : http://incompleteideas.net/sutton/book/bookdraft2016sep.pdf , By Richard S. Sutton and Andrew G. Barto

Courses

  1. Richard Sutton , CMPUT 366 - Intelligent Systems (Uni of Alberta) : https://www.dropbox.com/sh/467termw7cc95zk/AAAA3Om4QHihZf8yf7riy-Zwa?dl=0
  2. Yaser S. Abu-Mostafa , Learning from Data (Caltech) , https://work.caltech.edu/telecourse.html
  3. Davild Silver, Reinforcement Learning (UCL,now at Deepmind Technologies Ltd), http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
  4. Lex Fridman , 6.S094: Deep Learning for Self-Driving Cars (MIT), http://selfdrivingcars.mit.edu/
  5. Jürgen Schmidhuber's personal page : http://people.idsia.ch/~juergen/