Gradient Descent

Overview

One of the central goals of machine learning is reducing errors.

When a model makes an incorrect prediction, it needs a way to improve. Gradient Descent is one of the most common techniques used to achieve this goal.

A helpful way to think about Gradient Descent is to imagine standing on a foggy mountain while trying to reach the lowest point in a valley. You cannot see the entire landscape, but you can determine which direction slopes downward. By repeatedly taking small steps downhill, you gradually move closer to the bottom.

Gradient Descent works in a similar way.

The model evaluates its prediction errors and makes small adjustments that move it toward better performance. Rather than attempting to find the perfect solution immediately, it improves gradually through many small corrections.

This process allows neural networks and other machine learning models to become increasingly accurate as training progresses.

Although the mathematics behind Gradient Descent can be complex, its purpose is simple: help the model learn by reducing mistakes.

Why It Matters

Gradient Descent provides a practical way for machine learning models to improve their predictions over time.

Real-World Example

A recommendation system gradually improves product suggestions by using Gradient Descent to reduce prediction errors during training.

Related Concepts

• Backpropagation
• Stochastic Gradient Descent
• Epoch
• Batch Size
• Model Training