Introduction to Calculus in Machine Learning

Calculus plays an important role in machine learning, providing the mathematical foundations for developing and optimizing models. This chapter establishes the groundwork for understanding how calculus is applied within machine learning, setting the stage for more advanced techniques.

Throughout this chapter, you will look into the fundamental elements of calculus that are integral to machine learning. We'll start with a focus on derivatives, which help us understand how functions change and are extensively used in optimization problems such as gradient descent. You will learn how to compute and interpret derivatives in the context of machine learning algorithms.

Next, we will get into integrals, which are essential for understanding accumulation functions and areas under curves. These concepts often arise in probability distributions and other areas where machine learning models require integration.

Furthermore, we will explore optimization techniques, an area where calculus excels in helping us find the best parameters for our models. Understanding these techniques is needed for tuning algorithms to achieve high performance.

By the end of this chapter, you will have a solid grasp of how calculus is used in machine learning, equipping you with the knowledge to tackle more complex topics and applications in subsequent chapters.