Examples of using These algorithms in English and their translations into Chinese
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We use these algorithms to choose an action.
That group doesn't need its own science team,but it needed these algorithms and needed to be able to use them easily,” he says.
These algorithms are combined to reach several surprisingly intelligent tasks.
The tricky bit has been translating these algorithms into the real-world applications.
However, these algorithms have already been proven to show bias in their risk analysis.
People also translate
On a small scale, the people and companies that create these algorithms are able to affect what they do and how they do it.
These algorithms have been combined to achieve several surprisingly intelligent tasks.
More importantly, just like humans, these algorithms have the ability to learn and adapt to different conditions.
These algorithms can make catastrophic systemic errors, putting innocent people in prison.
They do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.
These algorithms define specific rubrics to choose an“interesting” linear projection of the data.
So there is an asymmetry between warming or cooling andhow well these algorithms can generalize, at least for the case of atmospheric convection.
These algorithms could systematically make calamitous mistakes and sending innocent, real humans to jail.
For a start, it means that any system created using these algorithms is going to perform worse for people from lower-income and non-Western countries.
These algorithms can make catastrophic systemic mistakes and send innocent people to prison in the real world.
Based on clinical studies, it is possible to use these algorithms to identify relevant biomarkers and to assess the statistical reliability of predictions.
These algorithms provide the conceptual backbone of almost every approach to the systematic exploration of alternatives.
To achieve this level of consistency, these algorithms are forced to tradeoff the availability of the data under certain failure scenarios.
And if these algorithms, like the algorithms on Wall Street, just crashed one day and went awry, how would we know?
Also called neural networks, these algorithms are especially good at detecting patterns across both noisy data and data that was once completely opaque to machines.
These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by.
You can see how these algorithms, meant to give you“more of what you want” can start creating echo chambers.
Using these algorithms, analog cameras not only shoot better images in low light conditions, but also provide better color reproduction and color compensation.
We might call these algorithms“dumb”, in the sense that they're doing their jobs according to parameters defined by humans.
Importantly, these algorithms and techniques are not limited to applications towards games, but also enable improvements in many domains.
For example, these algorithms can utilize the benefits of quantum computation to enhance the capabilities of classical techniques in machine learning.
Cirq supports running these algorithms locally on a simulator, and is designed to easily integrate with future quantum hardware or larger simulators via the cloud.
These algorithms are available from the well-known network library netlib as the package"Freely Distributable Math Library"(fdlibm).
Cirq supports running these algorithms locally on a simulator, and is designed to easily integrate with future quantum hardware or larger simulators via the cloud.