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- Added package structure documentation explaining modules/source/ vs tinytorch.core. - Enhanced mathematical foundations with linear algebra refresher and Universal Approximation Theorem - Added real-world applications for each activation function (ReLU, Sigmoid, Tanh, Softmax) - Included mathematical properties, derivatives, ranges, and computational costs - Added performance considerations and numerical stability explanations - Connected to production ML systems (PyTorch, TensorFlow, JAX equivalents) - Implemented streamlined 'tito export' command with automatic .py → .ipynb conversion - All functionality preserved: scripts run correctly, tests pass, package integration works - Ready to continue with remaining modules (layers, networks, cnn, dataloader)