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- Reorganize hidden solutions to follow correct NBDev pattern (after each student implementation) - Add comprehensive visualization system with _should_show_plots() control - Implement smart test/development mode detection to suppress visualizations during pytest - Add individual activation function plots and sample data visualizations - Include comprehensive comparison plots for all activation functions - Maintain educational inline tests with immediate feedback - All 24 tests pass cleanly with proper visualization control - Follows same structure as layers and tensor modules Features: - ReLU, Sigmoid, Tanh, Softmax visualizations with mathematical properties - Real-time visual feedback during development - Clean test execution without visual noise - Educational context and use-case guidance for each activation