Mathematics of Neural Networks
This document provides a comprehensive overview of the mathematics behind neural networks, starting with the basics of supervised learning and progressing to advanced topics like deep learning, convolutional neural networks, and the novel concept of equivariant tropical operators. It explains key concepts like activation functions, gradient descent, and backpropagation, offering detailed examples and mathematical formulations. The document also explores how geometric transformations can be integrated into neural network design for tasks requiring specific symmetries.