Parameters vs Hyperparameters in Neural Network
1. Story-Like Example
Imagine a chef in a kitchen trying to bake the perfect cake (the neural network is that cake).
- Chef has ingredients: sugar, flour, eggs, etc. These are like parameters.
- Chef chooses how long to bake, what temperature to use, how many layers to make — these are like hyperparameters.
2. Parameters vs Hyperparameters
| Concept | Analogy in the Story | Neural Network Equivalent |
|---|---|---|
| Parameters | The amount of sugar or flour used | Weights and biases learned by the model |
| Hyperparameters | Oven temperature, baking time | Settings like learning rate, number of layers, batch size |
| Who sets it? | Ingredients: adjusted by tasting (internally) | Learned automatically during training |
| Who chooses it? | Baking style: chosen by chef | Set manually by humans before training |
3. Definitions
| Term | Definition |
|---|---|
| Parameters | Internal weights and biases the network learns from data. |
| Hyperparameters | External configurations set before training that control how learning happens. |
Parameters vs Hyperparameters in Neural Network – Parameters vs Hyperparameters with Simple Python