Powering Neural Networks: Implementing Linear Transformations and Softmax

Linear transformations and softmax are mathematical primitives that appear in virtually every layer of modern neural networks. Understanding their implementation details is critical for developers building AI systems, especially since performance choices between Debug and Release builds can dramatically impact training speed. These pure math utilities form the computational foundation that all advanced neural network architectures rely upon.