👉 Layer math, also known as neural network layers, is the backbone of deep learning models, where data flows through a series of interconnected layers to transform it into meaningful representations. Each layer applies a specific mathematical transformation, such as linear combinations with weights and biases, followed by non-linear activation functions. The input layer receives raw data, while subsequent layers progressively extract higher-level features—from simple patterns in the first layers to complex abstractions in deeper layers. This hierarchical processing enables models to learn intricate relationships in data, making them powerful tools for tasks like image recognition, natural language processing, and more. The "layer" aspect emphasizes the sequential, modular nature of these transformations, where each layer builds on the output of the previous one to produce a refined, layered understanding of the input.
