Which of the following is an example of a model parameter?Review LaterLearning rateRegularization strengthNumber of hidden layersWeights and biases

The example of a model parameter from the options given is "Weights and biases".

Here's why:

1. Learning rate: This is not a model parameter but a hyperparameter that you can adjust in the process of training your model. It determines how much the model changes in response to the estimated error each time the model weights are updated.

2. Regularization strength: This is also a hyperparameter. It is used to prevent overfitting, which is when the model learns the training data too well and performs poorly on new, unseen data.

3. Number of hidden layers: This is a part of the model architecture, not a parameter. It's something you decide on before training the model