What are the common hyperparameters used in different AI models?

What are the common hyperparameters used in different AI models?

Common hyperparameters include learning rate, batch size, and number of epochs. Each model may have specific ones too.

Can you explain the learning rate and its impact?

The learning rate controls how much to change the model in response to the estimated error each time the model weights are updated. A high learning rate can lead to convergence issues, while a low rate may slow down training.

What about batch size? How does it affect training?

Batch size determines the number of training examples utilized in one iteration. Smaller batches provide a more accurate estimate of the gradient but can be noisy, while larger batches can speed up training but may lead to less generalization.

Are there hyperparameters specific to certain models?

Yes, for instance, convolutional neural networks often use kernel size and stride, while recurrent neural networks may have parameters like sequence length.