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