1、培訓過程中，如有部分內容理解不透或消化不好，可免費在以后培訓班中重聽；
        2、培訓結束后,授課老師留給學員聯系方式,保障培訓效果,免費提供課后技術支持。
        3、培訓合格學員可享受免費推薦就業機會。

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...