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Towards Data Science
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From a Point to L∞
- The article discusses the differences between L₁ and L₂ norms and their significance in shaping models and measuring error in AI.
- It explores when to use L₁ versus L₂ loss and how they affect model regularization and feature selection.
- The concept of mathematical abstraction is highlighted as a key aspect in understanding norms like L∞.
- L₁ and L₂ norms play different roles in optimization and regularization, affecting the behavior of models.
- The article delves into L₁ and L₂ regularization methods like Lasso and Ridge regression, explaining their impact on model sparsity and generalization.
- It shows how the choice between L₁ and L₂ loss can influence outcomes in Generative Adversarial Networks (GANs) in terms of image output.
- The generalization of distance to Lᵖ space is discussed, leading to the introduction of the L∞ norm.
- The L∞ norm, also known as the max norm, is characterized by its limit and utility in providing a uniform guarantee.
- Various real-world applications of the L∞ norm are highlighted, showcasing its importance in different contexts.
- The article concludes by emphasizing the importance of understanding distance measures and their implications in modeling decisions.
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