Which method improves efficiency in deep learning as the amount of data increases?

Neural networks are particularly adept at handling large datasets in deep learning due to their architecture and capacity for non-linear transformations. As the quantity of data increases, neural networks can learn and generalize complex patterns more effectively, making them highly scalable. Their layered structure allows for the extraction of hierarchical features, which can improve performance significantly when exposed to extensive datasets.

In contrast, the other methods listed play different roles in data processing or analysis rather than directly enhancing efficiency as data volume increases. Data pruning focuses on reducing the dataset size by removing irrelevant data without necessarily improving efficiency with larger datasets. Data normalization adjusts the scale of data features but does not inherently increase the learning capability or efficiency of the model as data grows. Static analysis is typically used in software development for analyzing code rather than in the context of efficiently improving deep learning performance. Thus, neural networks are the most suited for leveraging large amounts of data effectively in deep learning.