TALABA BILIMINI BAHOLASHDA XEMMING NEYRON TO‘RIDAN FOYDALANISH
Keywords:
O‘qitish modeli, baholash modeli, modellashtirish, aktivlantirish funksiyasi, Xemming masofasi, Bais, binary vektor, neyron to‘r, baholash kriteriyasi, vazn koeffitsiyent, iteratsiya.Abstract
XXI asrga kelib globallashib borayotgan dunyoda IT, ilm- fan, texnika va boshqa bir qator sohalarda “ Sun’iy intellekt “ atamasi keng qo‘llanila boshlandi. 2019- yilda dunyoda keng tarqalgan pandemiyasi tufayli sun’iy intellekt texnologiyalarini takomillashitirish va bir qator sohalarga tadbiq etish ehtiyoji tug‘ildi. Xususan ta’lim tizimida–sun’iy intellektning boshqaruv jarayonlarini avtomatlashtirish, o‘quv jarayonini optimallashtirish va o‘quvchilarning mustaqil ta’limini rivojlantirish kabi afzalliklari namoyon bo‘la boshladi. Ushbu maqolada Xemming neyron to‘ri orqali sun’iy intellekt yaratish masalasi ko‘rib chiqildi va u talabalar bilimini baholash misoli asosida yoritildi.
References
Hamet P., Tremblay J. Artificial intelligence in medicine //Metabolism. – 2017. – Т. 69. – С. S36-S40.
Юсупов Д.Ф. Нейрон тўрлари ва ундан ўқув жараёнида фойдаланиш
муаммолари. Монография. – Урганч: «УрДУ», 2018. – 88 Б.
Kriegeskorte N., Golan T. Neyron tarmoq modellari va chuqur o‘rganish // Hozirgi biologiya. – 2019. – T. 29. – Yoʻq. 7. – P. R231-R236.
Abdukadirov A. A., Yusupov D. F. FORMALIZATION OF PREDICATES FOR BUILDING NEURON NETWORK IN RESEARCHING THE BASIS OF ALGORITHMIZATION A PROGRAMMING A INFORMATICS COURSE //European Journal of Natural History. – 2018. – №. 6. – С. 53-55.
Ю. Д. Фирнафасович, “ИНФОРМАТИКА ФАНИНИ ЎҚИТИШНИ НЕЙРОТАРМОҚ ТЕХНОЛОГИЯСИ АСОСИДА ТАШКИЛ КИЛИБ ТАЛАБАЛАРНИ ФАОЛЛАШТИРИШ МЕТОДИКАСИ,” pp. 2181–1717, 2021, doi: 10.53885/edinres.2021.25.64.100.
Юсупов Д.Ф. Таълим сифатини таҳлил қилишнинг ахборотлашган тизимини
яратишда сунъий нейрон тўрларини қўллаш// Илм сарчашмалари. – Урганч, 2018. -№ 2. – Б. 73-76.
Firnafasovich Y. D., qizi Samariddin R. S. N. Oliy ta’lim muassasalarida talabalar bilimini neyron tarmoqlar yordamida baholashning afzallik va kamchiliklari //Journal of Science-Innovative Research in Uzbekistan. – 2023. – Т. 1. – №. 5. – С. 405-410.
Ro‘zikajon Q., Sevara R. DATA MINING TECHNIQUES IN NATURAL LANGUAGE PROCESSING: A REVIEW //COMPUTER LINGUISTICS: PROBLEMS, SOLUTIONS, PROSPECTS. – 2023. – Т. 1. – №. 1.
Madatov K., Matlatipov S., Aripov M. Uzbek text’s correspondence with the educational potential of pupils: a case study of the School corpus //arXiv preprint arXiv:2303.00465. – 2023.
Akhmedovich K. M., Beknazarovna S. S. METHODS OF CHECKING THE GIVEN LITERATURE ON THE INTELLECTUAL POTENTIAL OF SCHOOLCHILDREN.
Sattarova S. B., Bekchanova F. X., Shermetov A. K. TERMINOLOGIK LUG‘AT YARATISH TEXNOLOGIYASI VA UNING TA’LIM TIZIMIDAGI AHAMIYATI //Academic research in educational sciences. – 2023. – Т. 4. – №. 5. – С. 422-434.
Khodjinazarovna B. F., Kamaliddinovich S. A., Beknazarovna S. S. VISUALIZING THE SOLAR SYSTEM USING PYTHON AND ITS IMPORTANCE IN EDUCATION //International journal of advanced research in education, technology and management. – 2023. – Т. 2. – №. 6.
Kuriyozov E. et al. Deep learning vs. classic models on a new Uzbek sentiment analysis dataset //Human Language Technologies as a Challenge for Computer Science and Linguistics. – 2019. – С. 258-262.
Kuriyozov E. et al. Deep learning vs. classic models on a new Uzbek sentiment analysis dataset //Human Language Technologies as a Challenge for Computer Science and Linguistics. – 2019. – С. 258-262.
