zoukankan      html  css  js  c++  java
  • [Machine Learning] Gradient Descent in Practice I

    Feature scaling: it make gradient descent run much faster and converge in a lot fewer other iterations.
     
    Bad cases:
    Good cases:

    We can speed up gradient descent by having each of our input values in roughly the same range. This is because θ will descend quickly on small ranges and slowly on large ranges, and so will oscillate inefficiently down to the optimum when the variables are very uneven.

    The way to prevent this is to modify the ranges of our input variables so that they are all roughly the same. Ideally:

    −1 ≤ x(i) ≤ 1

    or

    −0.5 ≤ x(i) ≤ 0.5

    These aren't exact requirements; we are only trying to speed things up. The goal is to get all input variables into roughly one of these ranges, give or take a few.

    Two techniques to help with this are feature scaling and mean normalization. Feature scaling involves dividing the input values by the range (i.e. the maximum value minus the minimum value) of the input variable, resulting in a new range of just 1. Mean normalization involves subtracting the average value for an input variable from the values for that input variable resulting in a new average value for the input variable of just zero. To implement both of these techniques, adjust your input values as shown in this formula:

    Example:

    (D)

  • 相关阅读:
    Orleans 2 实例
    Linux基础1 目录和文件系统
    C#中的异步多线程补充1
    委托的小例子(基本委托,匿名方法,lambda)
    Orleans 1 基本概念
    WPF10 Binding-2
    WPF9 Binding-1
    WPF8 UI布局
    WPF7 布局控件
    软工总结
  • 原文地址:https://www.cnblogs.com/Answer1215/p/13546179.html
Copyright © 2011-2022 走看看