Description
Imagine an athlete who excels in practice but crumbles under the pressure of a real competition. Similarly, a deep learning model may achieve impressive performance on its training data but fail to generalize to unseen examples. Generalization, the ability to perform well on new data, is the ultimate test of a model’s effectiveness.
Here are the essential techniques for evaluating generalization in deep learning models. From splitting your data strategically to employing regularization and advanced techniques, here’s how to ensure your models are robust, reliable, and ready to tackle real-world challenges.
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