What sets deep learning apart from traditional neural networks?

Deep learning is distinguished from traditional neural networks largely due to its architecture, specifically the use of multiple layers of neural networks. This stacked structure allows deep learning models to learn complex patterns and representations in data that simpler architectures may not be able to capture effectively.

The multiple layers facilitate hierarchical feature extraction, where lower layers of the network learn to identify simple patterns, and as data passes through deeper layers, the network can recognize more complex patterns. This depth in the neural network is fundamental to the capabilities of deep learning models, enabling them to perform well in tasks such as image and speech recognition.

Other aspects, such as cloud computing integration, computational efficiency, or linear transformations, while relevant in various contexts, do not specifically define deep learning in contrast to traditional neural networks. The defining feature is indeed the depth provided by multiple layers, which is why this choice accurately highlights a core characteristic that sets deep learning apart.