Deep learning is fundamentally based on neural networks characterized by multiple layers, which is what distinguishes it from traditional machine learning. The term "deep" specifically refers to the depth of these networks, meaning the number of layers, rather than the overall size of the network or the computing power required. While increased computing power and memory are essential for implementing deep learning effectively, they are not defining features of the concept itself. The complexity introduced by the numerous layers in deep learning models can complicate the interpretability of how these networks derive their conclusions.