Parameters vs Hyperparameters
What numbers does the AI learn, and which do YOU choose?
You pick hyperparameters first. Training updates parameters. After training, parameters stay fixed.
In simple words
Parameters (the weights) are numbers the AI learns by itself. Hyperparameters are knobs you turn before training starts, like oven temperature.
The real definition
Parameters are weights the model learns from data during training. Hyperparameters are settings you choose before training begins to control how learning happens.
Like… Making a Cake
Hyperparameters are your recipe choices: oven heat, baking time. Parameters are the cake's final texture and taste—they come from baking, you do not choose them directly.
But: You cannot change a baked cake's taste. You can adjust hyperparameters before retraining with new data.
You see it every day
Photo recognition
Your phone uses hyperparameter "learning speed" set by engineers. The AI learns parameters (edge weights) from millions of training images.
Weather forecasting
Scientists choose hyperparameter "update rounds" first. Then the model learns parameters from past weather data.
Loan decisions
A bank sets hyperparameter "penalty strength" before training. The AI learns parameters linking income and credit score to risk.
Step by step
- 1
Choose hyperparameters
You pick learning speed, update rounds, and other AI settings.
- 2
Feed in training data
Provide examples to help the AI learn what is correct.
- 3
AI adjusts parameters
The model changes its weights (parameters) based on errors.
- 4
Check and repeat
Test the model. Adjust hyperparameters if results are poor.
Remember
Memory trick
Parameter = (P)roduct of learning. Hyperparameter = (H)uman choice before start. You set H, AI learns P.
Words
- parameter puh-RAM-uh-ter
- A number the AI learns from training data.
- hyperparameter HY-per-RAM-uh-ter
- A setting you choose before training the AI.
- training TRAIN-ing
- The process where AI learns from data examples.
- learning speed LER-ning SPEED
- How fast the AI updates its parameters each step.
- weight WAYT
- A parameter that shows importance of one piece of data.
- model MAH-dul
- The trained AI program ready to make predictions.
Check yourself
Which one does the AI learn automatically from data?
Hint
Think: which number changes as the AI learns?
Parameters are the weights created during training.
You are building a photo AI. You must decide learning speed before training starts. Is this a parameter or hyperparameter?
Hint
Hyperparameter = your choice before starting.
Learning speed is a setting you set before training begins.
Why might you change a hyperparameter if your AI model gives poor results?
Show a good answer
Hyperparameters control how the AI learns. Different settings help it learn better from the training data. You adjust them to improve accuracy.
Tell a friend
“Parameters are what the AI learns; hyperparameters are what you control before it learns.”
People also ask
Can you change parameters after training?
Parameters are locked after training. To change them, you must train again with new data or new hyperparameters. Hyperparameters you can adjust before each training run.
What happens if you pick bad hyperparameters?
Bad hyperparameters make the AI learn poorly or slowly. The model gives worse results. You test and adjust hyperparameters to find better settings.
How many hyperparameters does an AI have?
Different AI types have different hyperparameters—usually 2 to 20 common ones. Learning speed, update rounds, and model size are typical examples.