Layers consist of neurons (and more commonly, neuron blocks). Deep neural networks are composed of multiple layers, with neurons in each layer connected to those in one or more adjacent layers. Increasing the number of layers makes a network "deeper." As a network deepens, its complexity grows, enhancing its predictive capabilities—but this also complicates training, as the solution space expands exponentially.
