What is hyperparameter optimization in deep learning?

What is hyperparameter optimization in deep learning?

Hyperparameter optimization is the process of tuning the parameters that govern the training of a deep learning model to improve its performance.

Why are hyperparameters important?

Hyperparameters can significantly affect the model's accuracy, training time, and overall performance, making their optimization crucial.

What are some common hyperparameters to optimize?

Common hyperparameters include learning rate, batch size, number of epochs, and architecture choices like the number of layers.

How can I optimize hyperparameters effectively?

You can use techniques like grid search, random search, or more advanced methods like Bayesian optimization to find optimal hyperparameters.