👉 Parameter computing refers to the process of utilizing and optimizing the parameters within machine learning models, which are essentially the weights and biases that define the model's behavior. These parameters are learned during training through algorithms like gradient descent, where they adjust to minimize the model's prediction error. In parameter computing, the focus is on efficiently managing and utilizing these parameters to enhance model performance, reduce computational costs, and improve scalability. This includes techniques such as parameter pruning, quantization, and knowledge distillation, which aim to reduce the number of parameters or the computational resources needed while maintaining or even improving model accuracy. Parameter computing is crucial in developing efficient and effective machine learning systems, especially for deploying models on resource-constrained devices.
