Episode 14: What Is “Learning Rate” That Determines How Fast AI Learns? A Gentle Explanation

What Is the “Learning Rate” That Determines How Fast AI Gets Smarter?

When talking about AI or machine learning, you may come across the term “learning rate.” In English, it’s called “Learning Rate,” and while it might seem like a minor detail at first glance, it actually plays a key role in determining how well—and how quickly—an AI can become smarter.

It’s one of those behind-the-scenes elements that quietly support the entire process. Once you understand it, you’ll find that the way AI grows and improves feels much more familiar and approachable.

A Mechanism That Decides How to Learn from Mistakes

So what exactly does “learning rate” mean?

In simple terms, it’s a number that decides how fast an AI learns from its mistakes. To explain a bit more, when an AI makes a prediction, it compares that prediction with the correct answer. Then, it gradually adjusts itself to reduce the difference between them. The learning rate controls how much adjustment is made each time.

This process uses something called “gradient descent.” You can think of it like climbing a mountain—step by step toward a better answer (the mountaintop). In this analogy, the size of each step—the stride—is determined by the learning rate.

Finding the Right Stride: A Mountain Climbing Analogy

Let’s imagine you’re climbing a mountain trail shrouded in fog. You can’t see far ahead, so you have to rely on your footing and move carefully one step at a time.

If your steps are too small, you’ll take forever to reach your goal. But if your steps are too big, you might lose balance or stray off course.

The same applies to AI. If the learning rate is too small, it takes far too long to reach a good solution. On the other hand, if it’s too large, the AI might go in the wrong direction or become unstable. That’s why finding that “just right” stride—meaning an appropriate learning rate—is so important.

However, this ideal stride isn’t easy to determine. It depends on factors like the data being used and the type of model involved. Sometimes it’s better to adjust as you go along. For this reason, techniques such as starting with a larger learning rate and gradually decreasing it over time have been developed. There are also systems that automatically adjust the learning rate (we’ll cover these in another article).

Still, at its core remains one key idea: The stride taken with each step—that’s what we call the learning rate.

A Concept That Resembles Our Own Learning Pace

Interestingly enough, this concept closely resembles how we humans learn as well.

When trying to learn something new, cramming everything in at once doesn’t always work. On the flip side, being overly cautious can slow things down too much. We often find ourselves repeating things at our own pace until they make sense—that’s kind of like finding our personal learning rate.

AI also grows through trial and error in its own way—not exactly like us—but still by making small adjustments over time. And even though it’s just one tiny number among many others in its system, that number carries meaning. The term “learning rate” quietly reflects this steady effort and rhythm of growth.

In our next article, we’ll look at another important idea for helping AI learn effectively: something called “batch size.” It’s all about setting a pace that allows steady progress without rushing or stalling. Let’s explore that together next time.