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• Machine Learning Limitations
  Machine Learning, Non-linear mappings
  Neural Networks
  Non-Linear Optimization, Stochastic/MiniBatch Gradient Decent
  Back Propagation
  Deep Sparse Coding
  Sparse Autoencoders (SAE)
  Convolutional Neural Networks (CNNs)
  Successes: Descriptor Matching
  Stereo-based Obstacle
  Avoidance for Robotics
  Pooling and invariance
  Visualization/Deconvolutional Networks
  Recurrent Neural Networks (RNNs) and their optimizaiton
  Applications to NLP
  RNNs continued,
  Hessian-Free Optimization
  Language analysis: word/sentence vectors, parsing, sentiment analysis, etc.
  Probabilistic Graphical Models
  Hopfield Nets, Boltzmann machines
  Deep Belief Nets, Stacked RBMs
  Applications to NLP, Pose and Activity Recognition in Videos
  Recent Advances
  Large-Scale Learning
  Neural Turing Machines