In this deep dive, we unpack sparse representation theory. Learn how a complex signal can be expressed with only a handful of dictionary atoms, why exact sparsity is NP-hard, and how convex relaxation (basis pursuit) and greedy methods (orthogonal matching pursuit) yield fast, provable solutions. We explore structured and collaborative sparsity, real-world impacts like faster MRI scans, and the growing link between sparse models and deep learning—showing how intelligent simplification drives clearer insights and smarter AI.Note: This podcast was AI-generated, and sometimes AI can make mistakes. Please double-check any critical information. Sponsored by Embersilk LLC

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