Convexity Explained — Why Non-Convex Deep Nets Still Train ML Interview Question

Convex problems come with a guarantee; neural nets throw it away and train fine anyway. This video explains convexity, why local equals global, and why saddle points — not bad minima — are the real obstacle. In this video you'll learn: - The chord definition and the Hessian condition - Convex losses vs non-convex nets - The saddle-point surprise, and Jensen's inequality 🎯 Standalone episode from the AI/ML Engineering Interview series. 💬 Ever watched two seeds land in different minima? #MachineLearning #Optimization #Convexity #DeepLearning #MLInterview #InterviewPrep