Examples of using Many algorithms in English and their translations into Chinese
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MLlib contains many algorithms and utilities.
Many algorithms can operate on different types of data;
Machine learning provides many algorithms to classify flowers statistically.
Many algorithms can be used for machine learning, but the most successful ones today are deep neural networks.
Machine learning provides many algorithms to classify flowers statistically.
This data mining problem hasbeen studied for more than 15 years, and many algorithms have been proposed.
You will have access to many algorithms and use them to accomplish different business goals.
Once we have broken down business problems into machine learning tasks,one or many algorithms can solve a given machine learning task.
You will have access to many algorithms and use them to accomplish different business goals.
In addition, the compute hardware they also talked about the drive works SDK,which contains many algorithms that can be used for autonomous driving.
There are many algorithms that can be used in order to learn, but on a high level they behave rather similarly.
Over the years, we have come across many algorithms used to hold victims' files hostage.
Many algorithms for evaluating job candidates identify statistical patterns in the characteristics of current employees.
Ability to deal with different types of attributes: many algorithms are designed to cluster intervalbased(numerical) data.
It matches many algorithms on trees, which consist of doing one thing with the value, and another thing with the children.
Because it is designed to handle large-scale data,this powerful library has many algorithms and utilities such as classification, regression, and clustering.
To the contrary, many algorithms display troubling differences in accuracy across race, gender, and other demographics.
Many algorithms take the form of technology neutrality to carry out special forms and hide discrimination in a bid to avoid human rights censorship.
A crucial issue is that many algorithms are a"black box" and the public doesn't know how they makes decisions.
Many algorithms used by AI aren't fully written by programmers, but instead rely on the machine“learning” as it sequentially tackles problems.
For decades, engineers built many algorithms for machine vision, yet those algorithms each fell far short of human capabilities.
Many algorithms are proprietary, meaning the exact details of how they were programmed- including the sources of data used to train them- are off-limits to independent scientists.
It is possible to have many algorithms to solve a problem, but the challenge here is to choose the most efficient one.
There are many algorithms used for making predictions, but not that all prediction analyses need to be solved with incomprehensible algorithms. .
Many existing algorithms offer strong statistical guarantees.
Similarly many database-searching algorithms exist, for example:.
TensorFlow has many optimization algorithms available for training.
As you will see, many regression algorithms have classification counterparts.
Reduction to Hessenberg form(the first step in many eigenvalue algorithms).