Description
Imagine a student who memorizes the textbook but fails to understand the underlying concepts. They might ace the exams but struggle to apply their knowledge in the real world. Similarly, in AI, overfitting occurs when a model learns the training data too well, capturing noise and irrelevant details, leading to poor performance on new, unseen data.
Regularization techniques act as a guiding hand, preventing your AI model from straying too far from the path of generalization. Here is an overview of regularization, its importance, various techniques, and how to apply them effectively to keep your AI models grounded and perform optimally in real-world scenarios.
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