Examples of using Convolutional in English and their translations into Slovak
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Images recognition in Matlab using a convolutional neural network.
Convolutional codes, by contrast, continuously add redundant bits and have an arbitrary length.
The use of visualisation in analysis of deep convolutional networks Bukovčiková, Z.
This is essential in a convolutional neural network, in which so many nodes are processing the same data.
A wide variety of ECCs have been developed, but they generally can be classified into two main types:block and convolutional.
In 2012, AlexNet won the convolutional neural network ImageNet competition.
The convolutional neural network takes the 2D orientation field of a hair image as input and generates strand features that are evenly distributed on the parameterized 2D scalp.
The result of the thesis confirms that deep convolutional neural networks are a suitable tool for driving lane borders recognition.
One important factor in the success of artificial neural networks is a significant increase in the depth of modern neural network models inconjunction with a reduced number of parameters based on the convolutional principle and optimized architecture.
We use artificial intelligence, convolutional neural networks, machine learning, adaptive algorithms, etc.
The team used a convolutional neural network to find the“biologically relevant” motion patterns in a large set of US health survey data and correlate that to both lifespans and overall health.
The current wave of AI is around sensing and perception- using a convolutional neural net to scan image and see if something of interest is there.
They built a massive convolutional neural network that models how the more than 19,000 proteins in the human body interact with one another and how different drugs interact with these proteins.
Another option, turbo code,is a block code built from two or more relatively simple convolutional codes plus interleaving that creates a more uniform distribution of errors.
It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in the internet of things in the future.”.
They already have 51 patents on the subject and,being aware of the nature of convolutional networks, I believe that the method can soon be emulated to a certain extent solely by software.
The way it works uses a deep neural network to create masks and partial convolutional predictions- in essence it creates an invisible layer that it manipulates until it‘feels' as though the image is complete.
A best of both worlds approachcombines the two types in concatenated coding schemes, in which the convolutional code performs the primary correction work and the block code subsequently catches leftover errors.
In this thesis we describe the techniques of deep learning, mostly the convolutional neural networks, which are the most significant method for processing images- detection, classification and segmentation of the image.
The scientists used deep learning(and Microsoft's COCO dataset)to train a convolutional neural network(CNN) how to recognize certain human features, called soft biometrics, using computer vision.
A new study from the University of Heidelberg found that a form ofartificial intelligence known as a deep learning convolutional neural network(CNN) was able to more accurately identify malignant and benign skin lesions than a fleet of dermatologists.
More specifically, AI models such as natural language processing canbe used to analyze the text description of an animal in a shelter, and convolutional neural networks can be used to analyze images of the animals and help determine the probabilities of animals being killed or adopted in different shelters.