Overview
Regularization is a technique used in deep learning to prevent overfitting, which occurs when a model learns the training data too well and performs poorly on new data. By adding a penalty for complexity to the loss function, regularization helps create simpler models that generalize better. Understanding regularization is crucial for building effective deep learning models that perform reliably in real-world applications.
📚 Key Learning Objectives
- ✓ Define regularization and its purpose in deep learning.
- ✓ Identify common types of regularization techniques.
- ✓ Explain how regularization affects model performance.
- ✓ Apply regularization methods to a sample dataset.
- ✓ Evaluate the impact of regularization on model accuracy.
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