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- Added detailed mathematical foundation of matrix multiplication in neural networks - Enhanced geometric interpretation of linear transformations - Included computational perspective with batch processing and parallelization - Added real-world applications (computer vision, NLP, recommendation systems) - Comprehensive performance considerations and optimization strategies - Connection to neural network architecture and gradient flow - Educational focus on understanding the algorithm before optimization