Calculus for Machine Learning: Derivatives and Integrals

Introduction:

Calculus is a fundamental branch of mathematics that plays a crucial role in machine learning, particularly in understanding and optimizing models.

Whether it’s calculating gradients during training or understanding how changes in input affect output, calculus provides the tools needed for these tasks.

This article will explore the concepts of derivatives and integrals, explain why they are essential in machine learning, and demonstrate how to use them with advanced examples.

1) What is Calculus?

Calculus is the mathematical study of continuous change. It is divided into two main branches:

Differential Calculus: Concerned with the concept of a derivative, which represents the rate of change of a function.

Integral Calculus: Focuses on the concept of an integral, which represents the accumulation of quantities.

These concepts are vital in machine learning for understanding the behavior of functions, optimizing models, and making predictions.

2) Why Do We Need Calculus in Machine Learning?

Calculus is essential in machine learning for several reasons: