分数阶导数的意义
钙衍生物(CALCULUS DERIVATIVES)
After derivative theory posts, we will start to see some of the applications that make this technique one of the most important in mathematics and therefore at machine learning.
在派生理论发表后，我们将开始看到使该技术成为数学中最重要的应用之一，因此成为机器学习中最重要的应用之一。
函数的最大值和最小值 (Maximum and Minimum of a function)
The theorem of the local maximum that we already introduced, can be extrapolated to the whole function domain.
我们已经介绍的局部极大值定理可以外推到整个函数域。
Let f be a function and A a set of numbers contained in the domain of f. A point x in A is a maximum point for f on A if f(x)≥f(y) for every y in A. The number f(x) itself is called the maximum value of f on A.
设f是一个函数， A是包含在f的域中的一组数字。 在A A点x是对于f的最大点上的一个，如果F(X)≥f(Y)，用于在每Y个。 f(x)本身称为A上f的最大值。
Notice that there can be multiple maximum points for the same function at distinct x values. The minimum point definition is obtained inverting the previous definition, ergo changing f(x)≥f(y) for f(x)≤f(y).
请注意，在不同的x值下，同一函数可以有多个最大点。 通过颠倒先前的定义获得最小点定义，对于f(x)≤f(y)进行ergo更改f(x)≥f(y) 。
最高和最低点的导数 (Derivatives at maximum and minimum points)
As you can expect, maximum and minimum points will always be a change in the derivative of the function, that allows us to demonstrate that:
正如你可以预期，最大我的妈妈和最低点永远是在衍生功能的变化，使我们能够证明：
Let f be any function defined on (a,b). If f is a maximum or a minimum point for