What are hyperparameters in deep learning and how do they differ from machine learning?

What are hyperparameters in deep learning and how do they differ from machine learning?

Hyperparameters are settings that govern the training process of a model. In deep learning, they include things like learning rate, batch size, and number of layers. In traditional machine learning, hyperparameters might include the number of trees in a random forest or the regularization strength in logistic regression.

Can you give examples of hyperparameters for both deep learning and machine learning?

Sure! For deep learning, examples include the number of epochs, dropout rate, and optimizer type. For machine learning, examples are the maximum depth of a decision tree, the number of clusters in K-means, and the kernel type in SVM.

How do I choose the right hyperparameters for my model?

Choosing the right hyperparameters often involves techniques like grid search, random search, or using algorithms like Bayesian optimization. It's also important to validate your choices using cross-validation.

What impact do hyperparameters have on model performance?

Hyperparameters significantly affect model performance. The right settings can lead to better accuracy and generalization, while poor choices can result in overfitting or underfitting.