



Explain the curse of dimensionality and how it relates to the risk of overfitting when dealing with high-dimensional data (many features).
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what is overfitting Informational
- How can we ensure that our validation set is representative of the data the model will encounter in the real world to get a reliable estimate of overfitting?
- What is the no free lunch theorem and its implications for overfitting?
- What are different feature selection techniques?
- Describe the potential risks of using too much data augmentation, such as introducing unrealistic or misleading examples.
- How does batch normalization indirectly help in reducing overfitting during the training of neural networks?
- What role does self-supervised learning play in learning representations that generalize well?
- How does dropout prevent overfitting in neural networks?
- How can the encoding of categorical features impact overfitting?
- What are some formal measures of model complexity, such as VC dimension and Rademacher complexity?
- How does fine-tuning of pre-trained models need to be done carefully to avoid overfitting?
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