👉 Computing layers refer to the hierarchical structure of processing units in deep neural networks, each designed to perform specific tasks. The foundational layer, often called the input layer, receives raw data and passes it to subsequent layers. Between these, one or more hidden layers exist, where complex computations occur through weighted connections and activation functions. These layers can range from simple linear transformations to intricate non-linear operations, enabling the network to learn hierarchical features and representations. The final layer, typically an output layer, produces the network's predictions or classifications. Each layer builds upon the previous one, with deeper layers capturing more abstract and high-level features, while shallower layers handle basic pattern recognition. This layered architecture allows deep learning models to effectively process and understand complex data, such as images, text, and audio, by progressively abstracting information.
