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- Added detailed mathematical foundation of function composition - Enhanced architectural design principles (depth vs width trade-offs) - Included real-world architecture examples (MLP, CNN, RNN, Transformer) - Comprehensive network design process and optimization considerations - Performance characteristics and scaling laws - Connection to deep learning revolution and hierarchical feature learning - Better integration with previous modules (tensor, activations, layers)