What is learningrate?
The learningrate is a small number that tells a machine‑learning model how big a step to take when it updates its internal settings (weights) during training. Think of it like the size of each stride you take while walking toward a goal.
Let's break it down
- Model parameters: the numbers the model adjusts to learn patterns.
- Error: how far the model’s predictions are from the true answers.
- Gradient: the direction that reduces the error the fastest.
- Learningrate: multiplies the gradient to decide how far to move the parameters in that direction each step.
Why does it matter?
If the learningrate is too large, the model may overshoot the best solution and bounce around or diverge. If it’s too small, learning becomes painfully slow and may get stuck in a mediocre spot. The right learningrate helps the model find a good solution efficiently.
Where is it used?
Learningrate is used in almost every training algorithm for neural networks, linear regression, logistic regression, and many other machine‑learning models that rely on gradient‑based optimization (e.g., SGD, Adam, RMSprop).
Good things about it
- Simple to understand and implement.
- Directly controls training speed and stability.
- Can be adjusted dynamically (learning‑rate schedules, decay, warm‑up) to improve performance.
- Works with a wide range of models and optimizers.
Not-so-good things
- Choosing the right value can be trial‑and‑error; a bad choice harms training.
- A single constant learningrate may not be optimal for all training phases.
- Some advanced optimizers hide the learningrate, making it harder to tune.
- Too high a learningrate can cause the model to diverge, while too low can waste compute resources.