👉 Avatar math is a mathematical framework that simplifies the complex interactions within neural networks by representing layers as interconnected nodes or "avatars." Each avatar corresponds to a specific function within the network, capturing the essence of its computations without needing to model every detail. This abstraction allows for efficient computation and analysis, as avatars can be combined, manipulated, and optimized independently. By focusing on the relationships between these avatars, researchers can better understand how information flows through the network, how it transforms with each layer, and ultimately, how the network makes predictions. This approach not only aids in interpreting neural networks but also facilitates the design of more efficient and interpretable models.