Examples of using Algorithms can in English and their translations into Chinese
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The key is that the signals in the data that these algorithms can see are too narrow or too wide for humans.
These algorithms can use less data on a per-byte basis to correct for more serious problems than is possible with 512-byte sectors.
These algorithms can be applied straight to a database or they can be called from their own Java code.
Doctors use their years of experience to do this on an anecdotal basis, but algorithms can do this prediction more powerfully, effectively and efficiently.
These algorithms can make catastrophic systemic mistakes and send innocent people to prison in the real world.
Despite claims that smart algorithms can make up for bad signals, they can do only so much.
In some cases, well-trained computer vision algorithms can perform on par with humans that have years of experience and training.
For example, these algorithms can utilize the benefits of quantum computation to enhance the capabilities of classical techniques in machine learning.
These algorithms can find the optimal balance between material savings and speed of operations.
Many algorithms can be used for machine learning, but the most successful ones today are deep neural networks.
Algorithms can produce actionable insights even though it is not yet possible to explain the reasons behind these insights.
Based on the traffic pattern, the decision-making algorithms can adjust the traffic lights and their duration and lodge complaints against violators of rules.
These algorithms can make catastrophic systemic errors, putting innocent people in prison.
Machines and algorithms can work with highly repetitive tasks with higher speed and quality.
Microsoft Azure has created a handy algorithm cheat sheet that shows which algorithms can be used for which category of problems.
Algorithms can solve a lot of problems but they cannot reach out to people at an emotional or human level.
Many learning algorithms can get distracted by certain tasks in the set of tasks to solve.
Algorithms can produce actionable insights even though it may not yet be possible to explain the reasons behind these insights.
Some times, the algorithms can easily parallelized and we have a tremendous speedup, others the effort needed doesn't justify the gain.
Powerful AI deep-learning algorithms can accurately predict what objects in the vehicle's travel path are likely to do.