ZAMONAVIY SUN’IY INTELLEKT ALGORITMLARI VA ULARNING ARXITEKTURAVIY TAHLILI
DOI:
https://doi.org/10.65164/2ajfp822Kalit so‘zlar:
Deep Learning, CNN, RNN, LSTM, Transformer.Abstrak
Ushbu maqolada zamonaviy sun’iy intellekt algoritmlarining nazariy asoslari, matematik modellari va amaliy qo‘llanilish sohalari tahlil qilinadi. Tadqiqotda klassik mashinaviy o‘rganish algoritmlari, jumladan Random Forest, Support Vector Machine (SVM) va Gradient Boosting/XGBoost modellarining ishlash prinsiplari ko‘rib chiqilgan. Shuningdek, chuqur o‘rganish (Deep Learning) arxitekturalari — CNN, RNN, LSTM, Transformer va Graph Neural Network (GNN) modellarining tuzilishi, afzalliklari va cheklovlari ilmiy jihatdan yoritilgan. Tadqiqot natijalari sun’iy intellekt algoritmlarining robototexnika, signal va tasvirlarga ishlov berish, aqlli materiallar hamda Big Data texnologiyalaridagi yuqori samaradorligini ko‘rsatadi. Bundan tashqari, Explainable AI (XAI) va Transfer Learning yondashuvlarining zamonaviy AI tizimlaridagi ahamiyati tahlil qilingan.
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