Deep Learning Generally Follows a Hierarchical Learning Structure

Deep learning typically adopts a hierarchical learning structure, which is theoretically grounded in simulating the workings of the human brain's cerebral cortex. The visual region of the cerebral cortex also operates hierarchically, with lower-level visual cortices being more sensitive to basic features. Consequently, feature learning is driven by numerous application demands and supported by biological neural theories, ensuring its significant role in the AI field. Some experiments indicate that features learned by certain feature learning methods often outperform other features; for instance, the ISA model discussed in this article is one such example. In code implementations, these hierarchical structures are often realized through stacked layers (e.g., convolutional, pooling, or fully connected layers), with activation functions like ReLU facilitating feature extraction at different abstraction levels.