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🔥 Core Features Implemented: - Gradient descent step function with proper parameter updates - SGD optimizer with momentum and weight decay - Adam optimizer with adaptive learning rates and bias correction - StepLR learning rate scheduler with step-based decay - Complete training integration with real convergence examples 🧪 Testing & Validation: - All unit tests passing for each optimizer component - Learning rate scheduler timing fixed and working correctly - Training integration demonstrates SGD vs Adam convergence - Comprehensive test suite covering all functionality �� Educational Structure: - Follows TinyTorch NBDev patterns with solution markers - Step-by-step implementation guidance with TODO blocks - Mathematical foundations with intuitive explanations - Real-world training examples showing optimizer behavior - Complete documentation and README ✨ Results: - SGD achieves perfect convergence: w=2.000, b=1.000 - Adam achieves good convergence: w=1.598, b=1.677 - All tests pass, module ready for student use - Sets foundation for future 09_training module