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Regularisation: A Deep Dive into Theory, Implementation and Practical Insights
- The blog delves deep into regularisation techniques, providing intuitions, math foundations, and implementation details to bridge theory and code for researchers and practitioners.
- Bias in models could lead to oversimplification and underfitting, resulting in poor performance on training and test data.
- Variance in models causes overfitting, performing well on training data but failing to generalize to unseen data.
- The bias-variance tradeoff shows the inverse relationship between bias and variance.
- A good model finds a balance between bias and variance for optimal performance on unseen data.
- Bias and Underfitting; Variance and Overfitting are related concepts but not interchangeable.
- Different regularisation techniques like L1, L2, and Elastic Net aim to mitigate overfitting by penalizing large weights.
- Regularisation helps find the sweet spot between overfitting and underfitting in models.
- Methods like L1, L2, and Elastic Net are discussed in the blog for regularising models.
- Implementation details on how to apply L1, L2, and Elastic Net Regularisation in practice are provided.
- The blog also touches upon Dropout, Early Stopping, Max Norm Regularisation, Batch Normalisation, and Noise Injection as regularization techniques.
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