Articles

KONVOLYUTSION NEYRON TARMOQLAR (CNN) VA QO‘LLANILISHI

Vol. 4 No. 34 (2026): Science and Innovation 45-46

DOI: 10.5281/zenodo.20093739 2026-05-08 Articles Open Access

Authors

  • Abdukadirov Baxtiyor Abduvaxitovich Farg'ona davlat universiteti Axborot texnologiyalari kafedrasi dotsenti PhD
  • Raxmatjonova SHaxzodaxon Komiljon qizi FarDu Axborot tizimlari va texnologiyalari yo‘nalishi 2-bosqich talabasi

Abstract

Ushbu maqolada konvolyutsion neyron tarmoqlar (CNN) ning nazariy asoslari, ishlash prinsiplari va amaliy qo‘llanilish sohalari tahlil qilinadi. CNN chuqur o‘rganish (deep learning) sohasining muhim yo‘nalishlaridan biri bo‘lib, ayniqsa tasvir va video ma’lumotlarni qayta ishlashda yuqori samaradorlikka ega. Maqolada CNN arxitekturasi, asosiy qatlamlari va ularning vazifalari yoritilgan. Shuningdek, ushbu texnologiyaning turli sohalardagi qo‘llanilishi va mavjud muammolari ham ko‘rib chiqilgan.

Keywords:

References

Goodfellow, I. (2016). Deep Learning.

LeCun, Y. (1998). Gradient-Based Learning Applied to Document Recognition.

Krizhevsky, A. (2012). ImageNet Classification with Deep Convolutional Neural Networks.

Russell, S., Norvig, P. (2021). Artificial Intelligence: A Modern Approach.

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Published

2026-05-08

How to Cite

Abdukadirov, B., & Raxmatjonova , S. (2026). KONVOLYUTSION NEYRON TARMOQLAR (CNN) VA QO‘LLANILISHI. Science and Innovation, 4(34), 45-46. https://doi.org/10.5281/zenodo.20093739
Innovative Academy RSC
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