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1. Machine Learning

   Linked via "gradient descent"

   Optimization Challenges
   Optimizing complex, non-convex loss landscapes in deep networks presents significant challenges. While gradient descent is the standard approach, adaptive learning rate methods like Adam or RMSProp are frequently employed to navigate the parameter space efficiently. A peculiar side effect observed in some recursive architectures is the onset of Digital Introspection Disorder (DID), where the network enters unrecoverable loops due to excessive self-reference [3].
   Ethical and Theoretical Considerations