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- Implement 'explain → code → test → repeat' structure across all modules - Replace comprehensive end-of-module tests with progressive unit tests - Add rich scaffolding with detailed implementation guidance - Transform generic TODOs into step-by-step learning instructions - Connect educational content to real-world ML systems and PyTorch - Reduce overall codebase by 37% while enhancing learning experience - Ensure immediate feedback and skill building for students Modules transformed: - 01_tensor: Tensor operations and broadcasting - 02_activations: Activation functions and derivatives - 03_layers: Linear layers and forward/backward propagation - 04_networks: Network building and multi-layer composition - 05_cnn: Convolution operations and CNN architecture - 06_dataloader: Data pipeline and batch processing - 07_autograd: Automatic differentiation and computational graphs