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"The past is always with us, and it shapes who we are."
In the context of computational learning, the concept of 'probably approximately correct' (PAC) learning provides a framework for understanding the efficiency and feasibility of learning algorithms.
A good learning algorithm should be able to handle noise and uncertainty in the data effectively.
如果你把问题反过来,问题往往会变得更容易。换句话说,如果你想帮助印度,你应该问的问题不是“我怎么能帮助印度”,而是“什么是对印度造成最大伤害的?什么会自动地造成最大的伤害,我怎么避免它?”