Parameter Count

Parameter count refers to the total number of parameters in a machine learning model, particularly in deep learning. Parameters are the elements of the model that are learned from the training data, including weights and biases. A higher parameter count often indicates a more complex model that can capture intricate patterns in data, but it may also lead to overfitting if not managed properly. Common use cases include evaluating the capacity of neural networks and comparing different models in terms of their complexity and performance.