Course Code: dlv
Duration: 21 hours
Prerequisites:

需要任何编程语言知识。熟悉 Machine Learning 不是必需的，但有益。

Overview:

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。

Course Outline:

深度学习与 Machine Learning 与其他方法

  • 当 Deep Learning 合适时
  • Deep Learning 的限制
  • 比较不同方法的准确性和成本

方法概述

  • 网络和层
  • 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
  • 卷积

方法和模型

  • 反向支柱，模块化模型
  • Logsum 模块
  • RBF净值
  • MAP/MLE 丢失
  • 参数空间变换
  • 卷积模块
  • 基于梯度的学习
  • 推理能量，
  • 学习目标
  • 主成分分析;NLL：
  • 潜在变量模型
  • 概率 LVM
  • 损失函数
  • 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

工具

  • Caffe
  • 张量流
  • R
  • Matlab的
  • 别人。。。
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