EP73: AI Regularization Techniques — How Senior Engineers Prevent Overfitting in Production

🎙️ Daily AI Interview Prep — Episode 73

Topic: AI Regularization Techniques Deep Dive — How Senior Engineers Prevent Overfitting and Build Models That Generalize in Production

In this episode, we cover:
• The core overfitting problem and why regularization matters
• L1 vs L2 regularization — when to use each and how they work
• Dropout — how it prevents co-adaptation and acts as ensemble learning
• Early stopping — the simplest and most universal regularizer
• Batch Normalization vs Layer Normalization as implicit regularizers
• Data augmentation — the most powerful regularizer
• Label smoothing for better-calibrated, less overconfident models
• Advanced: Stochastic Depth, DropPath, and SAM optimizer
• Interview framework: diagnose → identify bottleneck → combine → tune
• 3 likely senior engineer interview questions with model answers

📌 Previous episodes: Transformers, Attention, Positional Encoding, Tokenization, Activation Functions, Layer Normalization, Loss Functions, Gradient Descent, Backpropagation

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Видео EP73: AI Regularization Techniques — How Senior Engineers Prevent Overfitting in Production канала Agentic AI World