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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...