在 英语 中使用 Does not equal 的示例及其翻译为 中文
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Political
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Ecclesiastic
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Programming
In other words, opposition does not equal subversion.
I understand correlation does not equal causation, but the increase in food allergies over the past several decades has to be caused by something.
Of course, correlation does not equal causation, and there are likely to be other factors involved.
If it does not equal -1, some eigenvalues and all eigenvectors are meaningless.
(Recommended reading:"Smart does not equal to excellent" era: Take off the school coat What do you have left?).
As SOA does not equal Web services and SOAP, different protocol bindings can be used to access services residing on different levels of abstraction.
If correlation does not equal causation, then how can we untangle the“why” behind processes that regulate the brain, the climate, or the economy?
It is always recommended to read all the data available for the chips because wattage does not equal brightness.
Darwin's theory does not equal progress, except in the sense that the new, evolved species will be better suited to their changing environment.
So exposure does not equal toxicity- it equals probability of toxicity at a sustained exposure.”.
Economic expansion does not equal imperialism, and there is no such thing as"cultural imperialism".
The sum does not equal the total sum of different contributions due to corrective entries made in the data exchange system.
A DHS-accredited SEVP school does not equal its full compliance with the F-1 visa and SEVIS requirements.
In spite of this, experts also reminded that laboratory success does not equal success in industrialization.
But Robbins will be the first to tell anyone,“Your past does not equal your future.”.
These benefits also work alongside Rose's two mantras; assessment drives programming, and a product does not equal programming.
As motivational speaker Anthony Robbins advocated,"The past does not equal the future.".
According to the experts,“claimed to be able” does not equal“guaranteed to work.”.
If it is a truism that causation does not equal correlation, the distinction between the two is also a serious concern for deep learning.