Deep Learning for Vision

Deep Learning vs Machine Learning vs Other Methods

• When Deep Learning is suitable
• Limits of Deep Learning
• Comparing accuracy and cost of different methods

• 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​

• 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

• Caffe
• Tensorflow
• R
• Matlab
• Others...