IntroductionAs deep learning models grow increasingly sophisticated, understanding the theoretical foundations that make them work becomes ever more critical. Two concepts stand out as particularly important for modern architecture design: inductive bias and knowledge distillation. These principles are not merely academic curiosities—they directly impact model performance, training efficiency, and practical deployment success.This article provides a comprehensive exploration of inductive bias and knowledge distillation, with special focus on...