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GMDH uses a "LEGO-style" structure, where it tests individual components (polynomial models) and combines only the best ones to build each layer automatically. In contrast, deep learning follows a "wired-style" structure, where all nodes are fixed in advance, and relationships are learned by optimizing weights. The fundamental difference lies in structure exploration versus weight optimization.