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python_exercise's Introduction

深度学习神经网络常用算法实现

  1. 梯度下降算法:正向传播以及反向传播实现
  2. 卷积神经网络算法
  3. 循环神经网络算法
  4. 长短时记忆网络
  5. 递归神经网络

传统算法数据结构实现

  1. 连通器算法
    • 这篇博客写的十分详细:http://blog.csdn.net/u010707039/article/details/51954033
    • 快速查找算法(quick-find)
      • 找出元素所在次序,并将其与相同元素标记。若元素标记一致,则在同一个合集内,跳过进入下一条。
    • 快速合并算法(quick-union)
      • 找出元素所在根,一开始所有元素对应的根为自己。
      • 表示核心:数组的下标对应节点,对应下标存储的值是父节点。
      • 寻找根节点,反复查询父节点,若父节点所对应值与下标一致,则为根节点。
      • 合并操作:将一个值挂在另一个值下。
    • 加权快速合并算法(weighted-quick-union)
      • 小幅度的修改快速合并算法,不再是任意挂取树,而是将小树挂在大树下面。
      • 解决了寻找根花费大量的时间。
      • 用一个size数组来记录每个根的长度,每次挂载的时候,将小树挂在大树下。
  2. 快速排序
    • 希尔排序
    • 快速排序
    • 二叉树
    • B树
    • 红黑树

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