



How can you ensure that your training, validation, and test sets are properly separated to avoid data leakage and get a reliable measure of generalization?
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what is overfitting Informational
- How does overfitting manifest in kernel methods like support vector machines (SVMs)?
- Consider convolutional neural networks (CNNs). What are some specific regularization techniques used to prevent overfitting, such as dropout and batch normalization?
- Is there a point where adding more data no longer helps with overfitting?
- How does the regularization imposed by the Kullback-Leibler (KL) divergence in VAEs help prevent overfitting?
- Describe the process of bagging. How does training multiple models on different subsets of the data and averaging their predictions lead to better generalization?
- How can we design machine learning systems that are both accurate and fair, and that generalize well across different populations and conditions?
- What role does pre-training play in mitigating overfitting in large language models?
- How do ensemble methods like bagging and boosting address overfitting?
- How do these measures of complexity relate to the generalization error of a model?
- What is the effect of the regularization parameter (lambda or alpha)?
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