Скачать или смотреть Regularization,over and underfitting Intuition Explained 🔥 | Must-Know for GATE DA & AI/ML Students

Are your machine learning models performing perfectly on training data but failing miserably on test data? 😬
That’s the classic case of overfitting — and the ultimate fix is Regularization.

In this video, I’ll simplify one of the most confusing ML concepts — L1 and L2 Regularization (Lasso & Ridge) — using visual intuition, geometry, and gradient descent insight.
You’ll finally understand why L1 pushes weights to zero, why L2 doesn’t, and how both reshape your loss surface to help your model generalize better.

📘 What You’ll Learn:

The difference between Overfitting vs Underfitting

What Regularization really does

How L1 (Lasso) and L2 (Ridge) penalties change model behavior

Geometric intuition using loss contours (circle vs diamond)

Gradient Descent update logic behind both

How λ (regularization strength) controls model simplicity

Why this is critical for GATE DA, AI/ML, and Data Science learners

🎓 Who should watch:
AI/ML students, GATE DA aspirants, and anyone who wants to feel how machine learning models actually learn — beyond just formulas.

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