Examples of using Machine-learning in English and their translations into Ukrainian
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reducing network volumes, and generating real-time machine-learning business insights.
Analytics-Driven:"Machine-learning and UEBA, used to develop aggregated risk scores that can also serve as hunting hypotheses"[4]
Bosch and Daimler machine-learning methods will generate vehicle-driving algorithms.
The underlying process and task can be mastered by current machine-learning paradigms.
To make accurate predictions, machine-learning algorithms need vast amounts of training data.
Koller co-founded Coursera with machine-learning expert and fellow Stanford professor Andrew Ng in 2012.
Unlike some machine-learning systems, Dabus has not been trained to solve particular problems.
Well, what if they replaced their hiring process with a machine-learning algorithm?
Neural networks and other machine-learning systems have become the most disruptive technology of the 21st century.
A typical machine-learning program will try to maximize overall prediction accuracy for the training data.
using machine-learning systems.
Machine-learning algorithms are really good at using statistics to find and apply patterns in data.
Another aspect under development is a machine-learning algorithm that will allow for better pattern recognition.
when not to use machine-learning algorithms.
which are optimized for machine-learning algorithms.
Holtzman wanted to know if machine-learning tools might detect something new in a gigantic dataset of earthquakes.
Machine-Learning Era in Cybersecurity:
This section presents some of the basic machine-learning concepts required for a description of early stopping methods.
These cloud-based machine-learning systems are designed to be a lot easier to use than the underlying algorithms.
They are in the dark because machine-learning programs do not reveal the rules they apply in drawing conclusions.