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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
安装
- 码头工人
- Ubuntu
- RHEL / CentOS / Fedora 安装
- 窗户
Caffe 概述
- Nets、Layers 和 Blobs：Caffe 模型的剖析。
- 前向/后向：分层组合模型的基本计算。
- 损失：要学习的任务由损失定义。
- 求解器：求解器协调模型优化。
- 层目录：层是建模和计算的基本单元 - Caffe 的目录包括用于最先进模型的层。
- 接口：命令行、Python 和 MATLAB Caffe。
- 数据：如何为模型输入的数据添加咖啡因。
- 含咖啡因的卷积：Caffe 如何计算卷积。
新模型和新代码
- 使用快速 R-CNN 进行检测
- 使用 LSTM 的序列和使用 LRCN 的视觉 + 语言
- 使用 FCN 进行像素级预测
- 框架设计与未来
例子：
- MNIST
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