Deep Learning for Vision with Caffe培训

课程编码

caffe

课程时长

21 小时 通常来说是3天,包括中间休息。

要求

None

课程概览

Caffe是一个深刻的学习框架,以表达,速度和模块化为基础。

本课程以MNIST为例,探讨了Caffe作为图像识别的深度学习框架的应用

听众

本课程适合有兴趣使用Caffe作为框架的Deep Learning研究人员和工程师。

完成本课程后,代表们将能够:

  • 了解Caffe的结构和部署机制
  • 执行安装/生产环境/架构任务和配置
  • 评估代码质量,执行调试,监控
  • 实施高级生产,如培训模型,实施图层和日志记录

Machine Translated

课程大纲

Installation

  • Docker
  • Ubuntu
  • RHEL / CentOS / Fedora installation
  • Windows

Caffe Overview

  • Nets, Layers, and Blobs: the anatomy of a Caffe model.
  • 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 – Caffe’s catalogue includes layers for state-of-the-art models.
  • Interfaces: command line, Python, and MATLAB Caffe.
  • Data: how to caffeinate data for model input.
  • Caffeinated Convolution: how Caffe computes convolutions.

New models and new code

  • Detection with Fast R-CNN
  • Sequences with LSTMs and Vision + Language with LRCN
  • Pixelwise prediction with FCNs
  • Framework design and future

Examples:

  • MNIST

 

 

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