Overview
Regularization is a technique used in deep learning to prevent overfitting, which occurs when a model learns the training data too well but performs poorly on new data. By adding a penalty for complexity, regularization helps create simpler models that generalize better. Understanding regularization is essential for building effective machine learning models that can make accurate predictions on unseen data.
📚 Key Learning Objectives
- ✓ Define regularization and its purpose in deep learning.
- ✓ Identify common types of regularization techniques.
- ✓ Explain how regularization helps prevent overfitting.
- ✓ Apply regularization methods in a deep learning model.
- ✓ Evaluate the impact of regularization on model performance.
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