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Related Questions
- What is the difference between l1 and l2 regularization, and how do they impact model complexity?
- How do regularization techniques like dropout and early stopping contribute to a more exploratory approach in gradient-based optimization?
- Can you explain how the L1 regularization term modifies the loss function to encourage sparse solutions?
- How does the L2 regularization term affect the loss function, and what are its implications for model generalizability?
- In what ways do regularization techniques help to prevent overfitting and promote a more exploratory search of the solution space?
- How do the hyperparameters of regularization techniques, such as the strength of regularization, impact the trade-off between exploration and exploitation?
- Can you provide examples of how regularization techniques are used in practice to balance exploration and exploitation in gradient-based optimization?
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