Neural Net: Number of Layers

You'll often hear, "X model has Y layers."

The number of layers in a neural network, particularly in a transformer architecture, indicates its depth. Each layer processes the data sequentially, refining the output with each pass.

Increasing layers: Typically enhances the model's ability to capture complex patterns and dependencies in the data, potentially improving performance on intricate tasks. However, it can also increase computational cost and the risk of overfitting.

Reducing layers: May decrease the model's capacity to learn complex representations, leading to faster computations and reduced risk of overfitting, but possibly at the cost of lower accuracy on complex tasks.

Source: ChatGPT 5/27/24