THE USE OF NEURAL NETWORKS IN THE FORMATION OF POLYCODE TEXT
https://doi.org/10.47649/vau.2022.v65.i2.05
Abstract
One of the ways to optimize the educational process is the presentation of educational material in the form of a polycode (creolized) text. Creolized text is a non-linear text, in the structuring of which, along with verbal, iconic means are used, as well as means of other semiotic codes (color, font, etc.), for example, in the form of non-verbal elements such as illustrations, graphic and pictorial means, diagrams, video and photo plots, visual images- drawings and videos. Selfcreation of the required content components. As a means of filling the content policedog text, you can use neural networks, in particular, announced in November 2021 neural network ruDALL-E of the company Yandex, which is able to create images based on text descriptions in different languages or neural network Imagen from Google that generates the image's description. The Imagen neural network was developed by the Google Brain research project team, which specializes in the study of artificial intelligence based on deep learning. Drawings Imagen created by the neural network from Google of good quality, but the range of options is limited to the proposed set of possible values When using the neural network ruDALL-E of the company Yandex, which is able to create images based on text descriptions in various languages. they are of good quality, while ruDALL-E is able to create high-quality images based on a text description in various languages. The article provides examples of the use of the above-mentioned neural networks to generate images based on a verbal description and concludes that they can be used for these purposes.
About the Author
A. MantusovRussian Federation
Candidate of Pedagogical Sciences, Associate Professor
Elista, 358003
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Review
For citations:
Mantusov A. THE USE OF NEURAL NETWORKS IN THE FORMATION OF POLYCODE TEXT. Bulletin of the Khalel Dosmukhamedov Atyrau University. 2022;65(2):42-51. (In Russ.) https://doi.org/10.47649/vau.2022.v65.i2.05