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ai_co's Introduction

ConvexOptim

记录凸优化学习中重点/疑问等.

第一章 机器学习与优化简介和关系

  • 1.1 机器学习与优化
  • 1.2 凸优化背景简介
  • 1.3 机器学习中优化问题和实例
  • 1.4 数学优化的形式化和示例
  • 1.5 凸优化求解和示例
  • 2. 应用简介:压缩感知/核磁共振成像/Netflix电影推荐/视频分割/投资优化组合
  • 3. 凸优化理论初步
  • 3.1 凸集定义与示例
  • 3.2 保持凸性运算
  • 分割超平面定理和支持超平面定理
  • 凸函数及其判定条件
  • 上镜图与水平子集
  • 保持凸性运算及Jensen不等式
  • 答疑

第二章 SVM与凸优化求解

  • 背景
  • 线性分类
  • 非线性分类
  • 松弛变量
  • 多元分类
  • 应用
  • 工具包
  • 代码演示
  • 答疑

第三章 DL与非凸优化

  • DL与非凸优化
  • DL简介
  • 非凸优化问题
  • DL训练技巧
  • 卷积神经网络
  • DL与卷积神经网络Demo
  • 答疑

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