Building on the concept of gradients from the previous chapter, we now focus on how to use them for model optimization. This chapter introduces gradient descent, a core iterative algorithm for finding the minimum of a function, typically the cost function $J(\theta)$ in machine learning.

You will learn the intuition and the specific steps involved in updating model parameters by moving in the direction opposite to the gradient $\nabla J(\theta)$. We will examine the role of the learning rate $\alpha$, compare different approaches like Batch, Stochastic, and Mini-batch gradient descent, and address common optimization issues such as local minima. The chapter includes a practical section on implementing the basic algorithm.