



How can oversampling and undersampling techniques affect overfitting?
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what is overfitting Informational
- How can the discriminator in a GAN overfit to the training data?
- How does mode collapse in GANs relate to the generator not generalizing well and overfitting to a limited set of training examples?
- Can explicitly modeling feature interactions increase the risk of overfitting?
- Explain how dropout works to prevent overfitting in neural networks.
- Describe the potential risks of using too much data augmentation, such as introducing unrealistic or misleading examples.
- What are some common data augmentation techniques for text data?
- How can we evaluate the quality of the representations learned through self-supervised learning?
- How can we build trust in machine learning systems by ensuring that they generalize reliably to new and unseen data?
- What are some techniques for handling overfitting in high-dimensional sparse data?
- Consider recurrent neural networks (RNNs). What are some specific challenges related to overfitting in RNNs, especially with long sequences?
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