👉 Remove computing is a novel approach to machine learning that aims to reduce the computational and energy costs associated with training large neural networks. Instead of training a full model from scratch, it involves distilling knowledge from an existing, larger "teacher" model into a smaller, more efficient "student" model. This process leverages the teacher's learned representations to guide the student's training, resulting in a compact model that retains much of the original model's performance while requiring significantly fewer computational resources and energy. This technique is particularly valuable in resource-constrained environments, such as edge devices or applications with limited computational power, making it a promising strategy for deploying advanced AI solutions more widely.
