For AI training to be considered deep learning, what does its neural network need more of?

Deep learning is a subset of machine learning that involves using neural networks with many layers to model complex patterns in data. For a neural network to be classified as "deep," it must consist of multiple layers of processing units (neurons). This depth allows the network to learn intricate features and representations of the data at different levels of abstraction.

The layers in a deep learning model generally include an input layer, one or more hidden layers, and an output layer. Each layer transforms the data, enabling the network to capture more complex relationships and patterns as it progresses through the various layers. The greater the number of layers, the more complex the relationships that can potentially be modeled, which is a core characteristic of deep learning.

While nodes, weights, and inputs are important for a neural network's function, having more layers is the defining attribute that differentiates deep learning from traditional machine learning approaches, which may utilize fewer layers or simpler structures.