Deep Learning Generally Follows a Hierarchical Learning Structure

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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.

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As mentioned in the text, deep learning is a hierarchical learning method based on simulating the working principles of the human brain's cerebral cortex. The visual region of the cerebral cortex operates across different layers, with lower-level visual cortices being more sensitive to basic features. In summary, feature learning has broad application demands in the AI field and is supported by biological neurology theories, ensuring it plays a crucial role in AI. Some experiments prove that features learned by certain feature learning methods are almost more effective than all other features; for example, the ISA model mentioned in this article is one such case. From a coding perspective, implementing hierarchical feature learning often involves constructing multi-layer neural networks where weights are optimized via backpropagation algorithms. The ISA model, for instance, can be coded using unsupervised learning techniques to capture invariant features through independent component analysis, enhancing robustness in tasks like image recognition.

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