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• Deep Learning vs Machine Learning vs Other Methods
  When Deep Learning is suitable
  Limits of Deep Learning
  Comparing accuracy and cost of different methods
  Methods Overview
  Nets and Layers
  Forward / Backward: the essential computations of layered compositional models.
  Loss: the task to be learned is defined by the loss.
  Solver: the solver coordinates model optimization.
  Layer Catalogue: the layer is the fundamental unit of modeling and computation
  Convolution?
  Methods and models
  Backprop, modular models
  Logsum module
  RBF Net
  MAP/MLE loss
  Parameter Space Transforms
  Convolutional Module
  Gradient-Based Learning
  Energy for inference,
  Objective for learning
  PCA; NLL:
  Latent Variable Models
  Probabilistic LVM
  Loss Function
  Detection with Fast R-CNN
  Sequences with LSTMs and Vision + Language with LRCN
  Pixelwise prediction with FCNs
  Framework design and future
  Tools
  Caffe
  Tensorflow
  R
  Matlab
  Others...