Gradient Descent Explained

Optimizing Models with Learning Rates and Loss Minimization

Gradient Descent is an optimization algorithm used to minimize loss in machine learning.

What is Gradient Descent?

1.

It iteratively adjusts model parameters in the direction of the steepest descent.

How It Works

2.

The cost function measures the difference between predicted and actual outcomes.

Cost Function

3.

The learning rate controls the size of each step taken towards the minimum.

Learning Rate

4.

Common types include Batch, Stochastic, and Mini-Batch Gradient Descent.

Types of Gradient Descent

5.

Batch Gradient Descent updates parameters after evaluating the entire dataset.

Batch Gradient Descent

6.

Used in training neural networks, regression models, and various AI applications.

Applications of Gradient Descent

7.