課程大綱： Advanced Deep Learning培訓
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
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