



What is the role of feedback and monitoring in continuously improving the generalization ability of deployed machine learning systems?
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what is overfitting Informational
- You are training a deep neural network, and you observe that the training loss is decreasing steadily, but the validation loss starts to increase after a certain number of epochs. What phenomenon are you observing, and how can you address it?
- In the context of model interpretability, how might overfitting affect our ability to understand the relationships learned by the model?
- What are the trade-offs between model complexity and overfitting?
- 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 reproducibility play in research on overfitting and generalization?
- Consider a support vector machine (SVM) with a non-linear kernel. How can choosing a kernel that is too complex lead to overfitting?
- What is the role of memory and capacity in the context of overfitting in neural networks?
- What are some advanced techniques for assessing the generalization ability of a model beyond simple train-validation splits, such as bootstrapping and permutation tests?
- How to debug and troubleshoot overfitting issues in machine learning models?
- What are some strategies for choosing appropriate kernels and regularization parameters in SVMs to avoid overfitting?
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