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✅ Core Features: - Variable class with gradient tracking and computational graph - Basic operations: add, multiply, subtract, divide with gradients - Advanced operations: power, exp, log, sum, mean with gradients - Activation functions: ReLU, Sigmoid with gradient computation - Chain rule implementation for complex expressions ✅ Performance & Utilities: - Gradient clipping to prevent exploding gradients - Parameter collection and gradient zeroing utilities - Memory-efficient gradient accumulation - Numerical stability for edge cases ✅ Comprehensive Testing: - 4,000+ lines of inline testing with educational explanations - 700+ lines of mock-based module tests (32 test cases) - Integration tests with neural network scenarios - Complete ML pipeline demonstration (linear regression) - Mathematical correctness validation ✅ Educational Features: - Step-by-step implementation with clear explanations - Real ML scenarios and applications - Visual feedback and progress tracking - NBGrader-compliant cells for coursework - Comprehensive documentation and examples ✅ Technical Implementation: - Computational graph construction and traversal - Automatic gradient computation using chain rule - Support for higher-order operations and compositions - Error handling and edge case management - Production-ready code quality The autograd module successfully enables automatic differentiation for neural network training, completing the foundation for TinyTorch's gradient-based optimization capabilities.