Gradient Descent
Gradient Descent
A method that gradually adjusts parameters to minimize the gap between an AI's predictions and the actual results.
In Simple Terms
So gradient descent is basically a mechanism that automatically figures out the settings that minimize prediction errors when an AI is learning. Based on the slope (gradient) of the calculated error, it gradually adjusts the parameter values in the direction that reduces the error. It's widely used across the training process of many AI models, including image recognition and text generation, to improve calculation accuracy.
Behind the Name
The name pretty much describes exactly what the method does: it follows the "gradient" (the slope at your current position) and "descends" step by step toward the lowest point, like walking downhill to reach a valley floor. It's also known as "steepest descent".
Take a Closer Look!
So gradient descent is a computational method that gradually searches for the parameter combination with the smallest calculation error, helping an AI get better at predicting things.
It's used as a core mechanism for training machine learning and deep learning models.
Picture it like walking down a mountain in thick fog, where you can't see ahead and can only rely on the slope right under your feet to find the lowest valley.
The AI calculates the gradient, the slope of the error at its current position, and gradually changes its settings in the downhill direction.
The size of each step it takes is called the learning rate, and by repeating these steps at an appropriate size, it efficiently approaches the valley with the smallest error.
Since it only relies on the slope right under its feet, it doesn't always end up reaching the single lowest valley overall.
Broadly speaking, there are several variations depending on how much data is used in each calculation step.
These include methods that use all the data at once, methods that use a single randomly chosen data point, and methods that process the data in small batches.
By choosing the right method for the use case and data scale, gradient descent supports large-scale AI training.