Deep Learning for Vision培训

课程编码

dlv

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

Any programming language knowledge is required. Familiarity with Machine Learning is not required but beneficial.

课程概览

听众

本课程适合有兴趣利用可用工具(主要是开源)分析计算机图像的Deep Learning研究人员和工程师

本课程提供了工作实例。

Machine Translated

课程大纲

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...

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