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��️ Major architectural improvement implementing clean separation of concerns: ✨ NEW: Activations Module - Complete activations module with ReLU, Sigmoid, Tanh implementations - Educational NBDev structure with student TODOs + instructor solutions - Comprehensive testing suite (24 tests) with mathematical correctness validation - Visual learning features with matplotlib plotting (disabled during testing) - Clean export to tinytorch.core.activations 🔧 REFACTOR: Layers Module - Removed duplicate activation function implementations - Clean import from activations module: 'from tinytorch.core.activations import ReLU, Sigmoid, Tanh' - Updated documentation to reflect modular architecture - Preserved all existing functionality while improving code organization 🧪 TESTING: Comprehensive Test Coverage - All 24 activations tests passing ✅ - All 17 layers tests passing ✅ - Integration tests verify clean architecture works end-to-end - CLI testing with 'tito test --module' works for both modules 📦 ARCHITECTURE: Clean Dependency Graph - activations (math functions) → layers (building blocks) → networks (applications) - Separation of concerns: pure math vs. neural network components - Reusable components across future modules - Single source of truth for activation implementations �� PEDAGOGY: Enhanced Learning Experience - Week-sized chunks: students master activations, then build layers - Clear progression from mathematical foundations to applications - Real-world software architecture patterns - Modular design principles in practice This establishes the foundation for scalable, maintainable ML systems education.