Overfitting vs. Double Descent: Why Bigger Models Are Better | Deep Learning Series скачать в хорошем качестве

Overfitting vs. Double Descent: Why Bigger Models Are Better | Deep Learning Series

Imagine a conspiracy theorist who connects random events—a car backfiring, a bird flying south, and the price of tea—into a perfect, complex story. They can explain the past perfectly, but they fail miserably at predicting the future. This is exactly what happens when your AI model Overfits. It memorizes the "noise" of your training data instead of learning the actual rules. In this Deep Learning deep dive (Episode 8), we move beyond the basic Bias-Variance Tradeoff to explore the modern paradoxes of AI. We explain why classical statistics says "simplify your model," but modern Deep Learning says "make it massive." You’ll learn about Double Descent—where increasing model size actually reduces overfitting—and Grokking, the mysterious phenomenon where models suddenly generalize after long periods of flatlined validation loss. Discussion: Have you ever killed a training run because the validation loss looked flat, only to realize later you missed a "Grokking" moment? Drop your "patience" stories in the comments! If this video saved you from building a "memorization machine," please like, subscribe to Sumantra Codes, and hit the bell for our next breakthrough. Chapters: 0:00 The Conspiracy Theorist Analogy (Overfitting) 1:30 Underfitting (High Bias) vs. Overfitting (High Variance) 3:15 The Classical Bias-Variance Tradeoff 5:00 Standard Fixes: L1/L2 Regularization & Dropout 7:45 The "Vanity Metric": Training vs. Validation Loss 9:20 The Paradox: Double Descent Explained 11:10 Grokking: Why You Need More Patience 13:45 Summary: Don't Build a Memorization Machine HASHTAGS: #DeepLearning #DoubleDescent #Overfitting #AI #MachineLearning #DataScience #Grokking #SumantraCodes