Приклади вживання The neural network Англійська мовою та їх переклад на Українською
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The neural network is now updated continually.
Scientists were able to reprogram the neural network of the brain.
The neural network has created a system of tracking traffic.
That combined data is what researchers used to train the neural network.
Introduction to the neural network. single-layer perceptron.
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His sphere of interests includes IT development, e-commerce solutions using the neural network.
During this time, the neural network has taken more than 3 million steps.
The neural network was trained to copy movements of people from videos on YouTube.
Now, social media content passes through the neural network determining suspicious activity.
Thus, the neural network will be taught during the traffic flow.
Neuroscientists have trained the neural network to translate brain signals into articulate speech.
The neural network of the brain can be seen as mental hardware.
After training with this vast collection of English, the neural network has presumably learned some useful things about language in general.
The neural network of the human brain is highly plastic and compensatory.
By learning on three-dimensional images, the neural network understands how flats are built and can create 3D images from ordinary photos.
The neural network is taught to almost perfectly replicate the human voice.
After collecting data on bestsellers, the neural network was able to predict future hits by 88.72%- this is more by 16.4% than traditional methods.
The neural network"Yandex" has improved the quality of old movies about the war.
After that, the neural network compared images to descriptions independently.
The neural network was trained on the basis of the collected information and tested.
NVIDIA has taught the neural network to remove unnecessary noise and inscriptions with photographs.
The neural network learned to write pictures on the basis of portraits of historical personalities.
Developers trained the neural network on thousands of manually processed images from family photo archives.
The neural network processed the data, produced clinical resolutions and compared them to diagnoses made by experienced and beginning doctors.
And if we assume that the neural network is able to look beyond the material world and the dimensions used to be known to human?
The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.
In this case, the neural network optimizes the color, contrast, brightness, exposure and other additional parameters of the camera when recognizing a photo.
As a result, the neural network learned to recognize depression with 77% of success, exceeding the performance of all other models based on questions and answers.
The neural network analyzes the features of the objects in these pictures and builds a recognition model that minimizes the percentage of errors relative to the reference results.