- By ogarci
- 0 comments
Higher-Order Derivatives in Machine Learning in ML
Higher-order derivatives have the ability to capture information about a function that first-order derivatives alone cannot.
First-order derivatives can capture critical information like the rate of change, but they can’t tell the difference between local minima and maxima with the same rate of change.
Lorem ipsum dolor sit amet consectetur adipisicing elit sed do eiusmod tempor incididunt labore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris aliquip commodo consequat.
Several optimization techniques use the usage of higher-order derivatives to overcome this limitation, such as Newton’s approach, which uses second-order derivatives to attain the local minimum of an optimization function.
The second-order derivative is the most commonly employed derivative in machine learning. We already discussed how the second derivative can offer us information that the first derivative alone cannot.
It can inform us whether a critical point is a local minimum or maximum (depending on whether the second derivative is higher or smaller than zero), while the first derivative would otherwise be zero in both circumstances.
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been.