

How can model pruning and quantization help in deploying large models on resource-constrained devices?
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what is overfitting Informational
- What are some tools and platforms that can aid in the reproducible training and evaluation of machine learning models?
- How are advances in theoretical machine learning contributing to our understanding of generalization and overfitting?
- How does adding more data help in reducing overfitting?
- What are some challenges in multi-task learning, such as negative transfer?
- What are some tools and libraries available for detecting and preventing overfitting in different programming languages?
- What are some emerging approaches to building more interpretable and trustworthy machine learning models that also generalize well?
- Is it always possible to completely eliminate overfitting, or is some level of overfitting often a trade-off for achieving good performance on the training data?
- In multi-task learning, how can sharing representations across multiple related tasks help to improve generalization and reduce overfitting on individual tasks?
- How does high variance typically manifest in an overfit model?
- What is structural risk minimization and how does it address overfitting?
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