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Related Questions
- What are some common techniques used to trade off between model size and accuracy in resource-constrained environments?
- How do different model complexity metrics, such as the number of parameters or computational cost, impact model accuracy?
- Can you discuss the role of regularization techniques, such as dropout or L1/L2 regularization, in reducing model complexity while maintaining accuracy?
- How do batch normalization and layer normalization impact model complexity and accuracy?
- What are some strategies for pruning or compressing models to reduce their size without sacrificing accuracy?
- Can you explain how knowledge distillation can be used to transfer knowledge from a larger, more complex model to a smaller one?
- How do you determine the optimal model size and complexity for a given problem and dataset in a resource-constrained environment?
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