Ensemble methods in machine learning refer to the technique of combining multiple models to improve the overall predictive performance. The idea behind ensemble methods is that combining the predictions of multiple models can lead to better accuracy and reduce the risk of overfitting.

Ensemble methods can be particularly useful in deep learning, where models are typically large and complex, and prone to overfitting. Here are some of the most popular ensemble methods used in deep learning:

Ensemble methods can also be used in deep learning to improve model robustness, increase diversity, and improve generalization performance. By combining multiple models, ensemble methods can help capture different aspects of the data and reduce the risk of models overfitting to specific patterns in the data. However, ensemble methods can be computationally expensive and require careful selection and tuning of the individual models used.