Hands-On Mathematics for Deep Learning

Derivatives

To start with, let's imagine a straight line with the following equation:

In the equation, the following aspects apply:

• y is a function of x, often written simply as f(x) (which is the notation we will be predominantly using in the remainder of the book). In the preceding equation, the output value y is dependent on the input value x.
• The m value is the gradient, which tells us how steep the straight line is, or what its rate of change is (that is, how much does a change in the x value affect the y value).
• The value tells us whether ...