Which application leverages Deep Learning techniques?

Facial recognition systems effectively utilize Deep Learning techniques due to their complexity and the nature of the data involved. Deep Learning, especially through neural networks, is particularly suited for processing large amounts of unstructured data such as images. In facial recognition, a convolutional neural network (CNN) analyzes the pixels of an image to identify patterns and features unique to each individual’s face. This allows the system to recognize and distinguish between different faces, even under varying conditions like changes in lighting or angle.

In contrast, basic text processing typically employs simpler algorithms and methods such as regular expressions or traditional machine learning techniques that do not require the depth of learning provided by neural networks. Simple data sorting is straightforward and can be managed with basic algorithms like bubble sort or quicksort, which do not involve learning from data. Traditional bookkeeping relies on established practices for managing accounts and financial records, which do not necessitate advanced Deep Learning approaches. Thus, facial recognition systems are the prominent application among the choices that leverage the capabilities of Deep Learning effectively.