Gradient Descent is an optimization algorithm used to minimize the cost function or loss function in machine learning and optimization problems. It is particularly useful in training machine learning models, such as linear regression, logistic regression, neural networks, and more complex models.

The primary goal of Gradient Descent is to find the minimum of a function by iteratively moving in the direction of steepest descent (i.e., the negative gradient of the function). In simpler terms, it's like finding the lowest point (minimum) on a surface by repeatedly taking steps in the direction of the steepest slope downward.

Here are the key steps involved in Gradient Descent:

1. Initialize Parameters: It starts by initializing the model parameters or coefficients with some initial values.

2. Compute Gradient: Calculate the gradient (partial derivatives) of the cost function with respect to each parameter. The gradient points in the direction of the steepest increase of the function.

3. Update Parameters: Adjust the parameters in the opposite direction of the gradient to minimize the cost function. The size of the update is determined by the learning rate, which is a hyperparameter that controls the size of the steps taken during optimization.

4. Iterate: Repeat steps 2 and 3 until convergence or until the algorithm reaches a stopping criterion (such as a specific number of iterations or a defined threshold for improvement).

There are different variants of Gradient Descent, including:

• Batch Gradient Descent: Uses the entire dataset to compute the gradient at each iteration. It can be slow for large datasets because it considers all data points at once.

• Stochastic Gradient Descent (SGD): Computes the gradient using only one random data point from the dataset at each iteration. It can be faster but may have noisy updates.

• Mini-batch Gradient Descent: A compromise between Batch GD and SGD, where it uses a small batch of data points to compute the gradient.

Gradient Descent is a fundamental optimization technique used in many machine learning algorithms to update model parameters iteratively and gradually improve the model's performance by minimizing the error or loss function. However, selecting an appropriate learning rate and handling convergence issues are critical considerations when using Gradient Descent in practice.