What gradient descent is
Gradient descent is an optimization method used to minimize a function by repeatedly moving toward its lowest point. In machine learning, it is the main technique for training models: it gradually adjusts the model's internal settings to reduce the error between the model's predictions and the correct answers. By taking many small steps in the right direction, gradient descent helps a model improve its performance over time.
The idea of going downhill
A helpful way to picture gradient descent is to imagine standing on a hilly surface and wanting to reach the lowest valley. At each step, you look at the slope beneath your feet and move downhill. Gradient descent does the same with a mathematical function: it measures the slope, called the gradient, and moves in the direction that decreases the function's value, repeating until it reaches a low point.
How it works in training
When training a model, gradient descent uses a measure of error, often called a loss, that tells how far the model's predictions are from the truth. It calculates how the error would change if the model's settings were adjusted, then nudges those settings in the direction that reduces the error. Repeating this process many times gradually tunes the model so its predictions become more accurate.
The learning rate
An important part of gradient descent is the learning rate, which controls how big each step is. If the steps are too large, the process can overshoot the lowest point and fail to settle. If they are too small, progress is very slow. Choosing a suitable learning rate is important for the method to work well, balancing speed against the risk of overshooting the target.
Variations of gradient descent
There are different versions of gradient descent that differ in how much data they use to compute each step. Some use all the available data at once, while others use small batches or even single examples at a time. These variations trade off speed, stability, and computational cost, and the choice depends on the problem. Despite the differences, they all share the same core idea of stepping toward lower error.
Why it matters
Gradient descent is the workhorse behind training most modern machine learning models, including neural networks. Understanding what gradient descent is - repeatedly adjusting a model to reduce its error by moving down the slope - clarifies how machines actually learn from data. For anyone curious about how AI models are trained, gradient descent is a foundational concept.
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See also: What is a neural network? Explained, How AI is trained, explained.
Sources
Published date reflects the original event date (2023-10-17). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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