SHAXSGA YO‘NALTIRILGAN TASVIRLAR VA VIDEOLARGA ISHLOV BERISHDA SUN'IY NEYRON TARMOQLARI ARXITEKTURASINI MATEMATIK MODELLASHTIRISH
DOI:
https://doi.org/10.5281/zenodo.21133719Keywords:
sun’iy neyron tarmoqlari, chuqur o‘rganish, matematik modellashtirish, tasvirlarga ishlov berish, videolarga ishlov berish, konvolyutsion neyron tarmoq, yo‘qotish funksiyasi, stilizatsiya.Abstract
Mazkur maqolada shaxsga yo‘naltirilgan tasvirlar va videolarga ishlov berishda qo‘llaniladigan sun’iy neyron
tarmoqlari arxitekturalarining matematik modellashtirish masalalari tadqiq etilgan. Tadqiqotning dolzarbligi raqamli media
texnologiyalarining jadal rivojlanishi, tasvir va video ma’lumotlari hajmining ortishi hamda ularni avtomatik qayta ishlash
jarayonlariga bo‘lgan ehtiyojning kuchayishi bilan izohlanadi. Ishda tasvirlardan shovqinni yo‘qotish, sifatini yaxshilash,
stilizatsiya qilish va videokadrlarni rekonstruksiya qilish jarayonlarida qo‘llaniladigan chuqur o‘rganish algoritmlarining
nazariy asoslari ko‘rib chiqilgan. Xususan, konvolyutsion neyron tarmoqlar (CNN), avtoenkoderlar, generativ adversarial
tarmoqlar (GAN) va transformator arxitekturalarining matematik modellari tahlil qilingan. Neyron tarmoq qatlamlari o‘rtasidagi
ma’lumotlar oqimi, parametrlarni optimallashtirish mexanizmlari hamda yo‘qotish funksiyalarining analitik ifodalari
o‘rganilgan. Tadqiqot davomida tasvir va videolarga ishlov berish samaradorligini oshirishga xizmat qiluvchi matematik
yondashuvlar ishlab chiqilib, ularning hisoblash murakkabligi va aniqlik ko‘rsatkichlari baholangan. Olingan natijalar sun’iy
intellekt asosidagi multimedia tizimlari, kompyuter ko‘rish texnologiyalari va shaxsga moslashtirilgan raqamli kontent
yaratish jarayonlarini takomillashtirishda muhim ilmiy-amaliy ahamiyat kasb etadi.
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