Examples of using Artificial neural in English and their translations into Romanian
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Artificial neural networks.
The data feeds are processed by an artificial neural network.
Artificial neural networks.
All you need to know about artificial neural networks.
Artificial neural networks.
Are you really as suspicious of artificial neural nets as you pretend?
We create artificial neural network for self-study and the search for the best interlocutors.
The characteristic feature is the AI technology(especially artificial neural network).
Then we will see if the artificial neural network is able to take over your cognitive functions.
Joone project is a FREE Neural Network framework to create,train and test artificial neural networks.
Deep learning is any artificial neural network that can learn a long chain of causal links.
BNNS is an open source(GPL)software that uses the Response Function Plots visualization method to train artificial neural networks.
Analysis of artificial neural network architecture and classifier education on limited MNIST dataset.
This is possible with the use of facial key-point detection framework for disguised face identification and the potential of artificial neural networks.
Artificial neural network validated for the design of complex parts made by additive technologies;
Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.
An artificial neural network consists in a large number of simple processing units, linked through weighted connections.
It employs an interconnected system of'artificial neural networks' which is constantly active while Entropy is'thinking'.
Artificial neural networks(ANNs) or connectionist systems are computing systems inspired by the biological neural networks that constitute animal brains.
The main research field of discipline includes robust control,intelligent system, artificial neural network, genetic algorithm, adaptive theory, non-linear control.
Lavric, Vasile(2012): Artificial Neural Network Modelling of Ultrasound and Microwave Extraction of Bioactive Constituents from Medicinal Plants.
Many tools are used in AI, including versions of search andmathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.
Artificial neural networks already run our internet search engines, digital assistants, self-driving cars, Wall Street trading algorithms, and smart phones.
Deep learning(also known as deep structured learning or hierarchical learning)is part of a broader family of machine learning methods based on artificial neural networks.
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence(AI) systems capable of performing"intelligent" tasks.
What actually stands behind artificial intelligence today can be briefly summarized by the syntagm„artificial neural networks“, to which the adjective„deep“ has recently been added.
An artificial neural network is non-linear, and thus an exceptionally powerful method for real-time data analysis that allows modeling extremely difficult dependencies.
To answer these challenges our team developed 4RES system providing means to forecast the production from RES by using artificial neural networks(layered networks, to be precise).
In the case of an artificial neural network, higher-level features correspond to more recognizable features, and enhancing these features brings out what the computer sees.
An agent that solves a specific problem can use any approach that works- some agents are symbolic and logical,some are sub-symbolic artificial neural networks and others may use new approaches.