AI Engineering Degree Practice Exam 2025 - Free AI Engineering Practice Questions and Study Guide

Gradient descent and learning rate have a crucial relationship in the context of logistic regression and other optimization algorithms. Gradient descent is an iterative optimization algorithm used to minimize the cost function by adjusting the model parameters in the opposite direction of the gradient (the slope) of the cost function. The gradient points towards the direction of the steepest ascent, so moving in the opposite direction allows the algorithm to find the minimum.

The learning rate is a hyperparameter that controls how much we adjust the model's parameters in response to the calculated gradient during each iteration. In simpler terms, while gradient descent determines the direction in which to adjust the parameters (towards the minimum of the cost function), the learning rate dictates how large of a step to take in that direction. A larger learning rate can lead to faster convergence but may also cause the algorithm to overshoot the minimum, potentially making it unstable. Conversely, a smaller learning rate provides more precise updates but can slow down the convergence process.

This interaction is fundamental in training models, as finding the right balance between the two is key to effectively optimizing the model parameters. Thus, understanding that gradient descent indicates the direction of the parameter update and the learning rate regulates the magnitude of this update is essential for successful implementation of logistic regression and other