1 Introduction Machine Learning(ML)has made significant progress in various tasks.This success is driven by powerful computational resources,expert experience,and,crucially,large-scale data.High-performance ML models ...1 Introduction Machine Learning(ML)has made significant progress in various tasks.This success is driven by powerful computational resources,expert experience,and,crucially,large-scale data.High-performance ML models typically require millions or even billions of samples.Training on such extensive datasets allows algorithms to capture complex patterns and improve generalization.Thus,large-scale data is essential for developing advanced ML models,driving the impressive advancements and implementations in the field today.展开更多
文摘1 Introduction Machine Learning(ML)has made significant progress in various tasks.This success is driven by powerful computational resources,expert experience,and,crucially,large-scale data.High-performance ML models typically require millions or even billions of samples.Training on such extensive datasets allows algorithms to capture complex patterns and improve generalization.Thus,large-scale data is essential for developing advanced ML models,driving the impressive advancements and implementations in the field today.
