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

萊斯利·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."

機器學(xué)習(xí)的力量在于它能夠發(fā)現(xiàn)數(shù)據(jù)中人類觀察者不易立即察覺的模式和關(guān)系。

萊斯利·瓦利安特

在機器學(xué)習(xí)中，目標(biāo)不僅僅是擬合數(shù)據(jù)，而是找到一個能夠捕捉數(shù)據(jù)潛在結(jié)構(gòu)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."

萊斯利·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."

在機器學(xué)習(xí)中，目標(biāo)不僅僅是擬合數(shù)據(jù)，而是找到一個能夠捕捉數(shù)據(jù)潛在結(jié)構(gòu)的模型。

萊斯利·瓦利安特

機器學(xué)習(xí)的成功取決于我們構(gòu)建的模型中偏差和方差之間權(quán)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."

萊斯利·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."

機器學(xué)習(xí)的成功取決于我們構(gòu)建的模型中偏差和方差之間權(quán)衡的能力。

萊斯利·瓦利安特

計算學(xué)習(xí)理論中的一個關(guān)鍵見解是，學(xué)習(xí)問題的復(fù)雜性由假設(shè)空間的復(fù)雜性和可用數(shù)據(jù)的數(shù)量決定。

"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."

萊斯利·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."

計算學(xué)習(xí)理論中的一個關(guān)鍵見解是，學(xué)習(xí)問題的復(fù)雜性由假設(shè)空間的復(fù)雜性和可用數(shù)據(jù)的數(shù)量決定。

萊斯利·瓦利安特

機器學(xué)習(xí)中的挑戰(zhàn)不僅僅是發(fā)現(xiàn)數(shù)據(jù)中的模式，而是發(fā)現(xiàn)能夠推廣到新的、未見過的數(shù)據(jù)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."

萊斯利·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."

機器學(xué)習(xí)中的挑戰(zhàn)不僅僅是發(fā)現(xiàn)數(shù)據(jù)中的模式，而是發(fā)現(xiàn)能夠推廣到新的、未見過的數(shù)據(jù)的模式。

萊斯利·瓦利安特

計算學(xué)習(xí)理論為理解機器學(xué)習(xí)算法的能力和局限性提供了一個數(shù)學(xué)框架。

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

萊斯利·瓦利安特

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

計算學(xué)習(xí)理論為理解機器學(xué)習(xí)算法的能力和局限性提供了一個數(shù)學(xué)框架。

萊斯利·瓦利安特

"The ultimate goal of machine learning is to make machines that can learn from experience and improve their performance over time."

機器學(xué)習(xí)的最終目標(biāo)是制造能夠從經(jīng)驗中學(xué)習(xí)并隨時間提高性能的機器。

萊斯利·瓦利安特

機器學(xué)習(xí)的未來在于開發(fā)能夠從更少的例子中學(xué)習(xí)并更有效地進行歸納的算法。

The future of machine learning lies in the development of algorithms that can learn from fewer examples and generalize more effectively.

萊斯利·瓦利安特

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