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✅ Fixed all forward dependency violations across modules 3-10 ✅ Learning progression now clean: each module uses only previous concepts Module 3 Activations: - Removed 25+ autograd/Variable references - Pure tensor-based activation functions - Students learn nonlinearity without gradient complexity Module 4 Layers: - Removed 15+ autograd references - Simplified Dense/Linear layers to pure tensor operations - Clean building blocks without gradient tracking Module 7 Spatial: - Simplified 20+ autograd references to basic patterns - Conv2D/BatchNorm work with basic gradients from Module 6 - Focus on CNN mechanics, not autograd complexity Module 8 Optimizers: - Simplified 50+ complex autograd references - Basic SGD/Adam using simple gradient operations - Educational focus on optimization math Module 10 Training: - Fixed import paths and simplified autograd usage - Integration module using concepts from Modules 6-9 only - Clean training loops without advanced patterns RESULT: Clean learning progression where students only use concepts they've already learned. No more circular dependencies!