莱斯利·瓦利安特的20句名人名言哲理格言警句语录

"The power of machine learning lies in its ability to discover patterns and relationships in data that are not immediately obvious to human observers."

莱斯利·瓦利安特

"The power of machine learning lies in its ability to discover patterns and relationships in data that are not immediately obvious to human observers."

机器学习的力量在于它能够发现数据中人类观察者不易立即察觉的模式和关系。

莱斯利·瓦利安特

在机器学习中，目标不仅仅是拟合数据，而是找到一个能够捕捉数据潜在结构的模型。

"In machine learning, the goal is not just to fit the data, but to find a model that captures the underlying structure of the data."

莱斯利·瓦利安特

"In machine learning, the goal is not just to fit the data, but to find a model that captures the underlying structure of the data."

在机器学习中，目标不仅仅是拟合数据，而是找到一个能够捕捉数据潜在结构的模型。

莱斯利·瓦利安特

机器学习的成功取决于我们构建的模型中偏差和方差之间权衡的能力。

"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."

莱斯利·瓦利安特

"The success of machine learning depends on the ability to balance the trade-off between bias and variance in the models we build."

机器学习的成功取决于我们构建的模型中偏差和方差之间权衡的能力。

莱斯利·瓦利安特

计算学习理论中的一个关键见解是，学习问题的复杂性由假设空间的复杂性和可用数据的数量决定。

"A key insight in computational learning theory is that the complexity of a learning problem is determined by the complexity of the hypothesis space and the amount of data available."

莱斯利·瓦利安特

"A key insight in computational learning theory is that the complexity of a learning problem is determined by the complexity of the hypothesis space and the amount of data available."

计算学习理论中的一个关键见解是，学习问题的复杂性由假设空间的复杂性和可用数据的数量决定。

莱斯利·瓦利安特

机器学习中的挑战不仅仅是发现数据中的模式，而是发现能够推广到新的、未见过的数据的模式。

"The challenge in machine learning is not just to find patterns in data, but to find patterns that generalize to new, unseen data."

莱斯利·瓦利安特

"The challenge in machine learning is not just to find patterns in data, but to find patterns that generalize to new, unseen data."

机器学习中的挑战不仅仅是发现数据中的模式，而是发现能够推广到新的、未见过的数据的模式。

莱斯利·瓦利安特

计算学习理论为理解机器学习算法的能力和局限性提供了一个数学框架。

"Computational learning theory provides a mathematical framework for understanding the capabilities and limitations of machine learning algorithms."