How does deep learning primarily find its features?

Deep learning primarily identifies features through neural networks without human guidance, which is the essence of how it operates. Neural networks consist of layers of interconnected nodes or "neurons" that process input data. As the data passes through these layers, the network automatically learns to detect patterns and features that represent the complexities of the data.

This process occurs through a method called backpropagation, where the network adjusts its parameters based on the errors of its predictions compared to the actual results. As a result, deep learning models can learn hierarchical features—starting from low-level features in early layers to more abstract and complex features in deeper layers—all autonomously, without requiring explicit instructions or predefined rules from humans.

The other provided choices represent approaches that involve more direct human intervention or limitations that are not characteristic of deep learning's capabilities. For instance, requiring detailed human input or working with predefined logical rules involves a supervised learning approach that lacks the flexibility and depth of feature extraction typical in deep learning. Similarly, analyzing static algorithm parameters does not reflect the dynamic, adaptive nature of deep learning models, which continuously adjust as they learn from the data they process.