What is model sparsity in deep learning, and why is it important?

Model sparsity refers to the condition where a neural network has a significant number of zero weights or inactive neurons, resulting in a more compact model with fewer active parameters. It is important because sparse models are typically more efficient, requiring less memory and computational resources for inference, which can lead to faster predictions. Additionally, introducing sparsity can help improve generalization by enforcing feature selection, subsequently reducing the risk of overfitting.