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          Deep Learning for Vision培訓(xùn)

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...