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SUN’IY INTELLEKT ASOSIDA MATEMATIK MODELLASHTIRISH: FORWARD PROPAGATION, LOSS FUNKSIYASI, BACKPROPAGATION VA GRADIENT DESCENT USULLARINING TO‘LIQ TAHLILI

Fan yo'nalishi:Sun'iy intellekt
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MAQOLA ANNOTATSIYASI

quote
Sun’iy intellekt va chuqur o‘rganish sohasida neyron tarmoqlarning o‘qitilishi to‘rtta asosiy bosqichdan iborat: forward propagation (oldinga tarqalish), loss funksiyasini hisoblash, backpropagation (orqaga tarqalish) va gradient descent (gradient tushish) usuli. Ushbu maqolada neyron tarmoqlarning matematik asoslari, har bir bosqichning nazariy va amaliy jihatlari, formulalar va algoritmlar batafsil yoritiladi. Forward propagation jarayonida ma’lumotlarning kirish qatlamidan chiqish qatlamigacha harakatlanishi, loss funksiyasi orqali xatoliklarni baholash, backpropagation algoritmi yordamida gradientlarni hisoblash va gradient descent usuli bilan og‘irliklarni yangilash jarayonlari matematik formulalar bilan tasvirlangan. Maqolada shuningdek, turli aktivatsiya funksiyalari, loss funksiyalari va optimizatsiya usullarining taqqosiy tahlili berilgan.

MUALLIFLAR

N.Otabek

"MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNOLOGIYALARI UNIVERSITETI" DAVLAT MUASSASASI

A.Murodjon

"MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNOLOGIYALARI UNIVERSITETI" DAVLAT MUASSASASI

A.Guzal

MIRZO ULUGʻBEK NOMIDAGI OʻZBEKISTON MILLIY UNIVERSITETI

Teglar

# matematik modellashtirish# sun’iy intellekt# backpropagation# chuqur o‘rganish# Neyron tarmoqlari# Forward propagation# Gradient descent# Loss funksiyasi# Optimizatsiya usullari

O'XSHASH MAQOLALAR

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Maqola idintifikatorlari

ROI:

https://eroi.uz/11.142/A026-667343

DOI:

Mavjud emas

Foydalanilgan adabiyotlar

Burden R. L., Faires J. D., Numerical Analysis, 10th Edition, Brooks/Cole, 2015.

Karimov F. T., “Differensial tenglamalar va ularni hisoblash usullari”, Toshkent: Universitet, 2015.

J. R. Chasnov, “Numerical Methods,” The Hong Kong University of Science and Technology. Hong Kong. pp. 60, 2021.

Alex Lewandowski, Varun Ranganathan ZORB: A Derivative-Free Backpropagation Algorithm for Neural Networks. November 2020. https://www.researchgate.net/publication/346015130_ZORB_A_Derivative-Free_Backpropagation_Algorithm_for_Neural_Networks

Chirag Agarwal, Joe Klobusicky, Don Schonfeld Convergence of backpropagation with momentum for network architectures with skip connections. https://arxiv.org/pdf/1705.07404

A.G. Baydin, B.A. Pearlmutter, D. Syme, F. Wood, P. Torr. Gradients without Backpropagation. https://arxiv.org/abs/2202.08587

G. Kim, Y. Bak. Forward and Backpropagation-Based Artificial Neural Network Modeling Method for Power Conversion System. https://www.mdpi.com/2079-9292/14/23/4718

A.B. Yemberdiyeva A.B. Yemberdiyeva, I.C. Young, M. Symbat, B.M. Samat “Mathematics approach of the backprogation method for constructing artificial neural networks. https://journal.iitu.edu.kz/index.php/ijict/article/view/442

Annette Lopez. Neural Networks: The Backpropagation Algorithm. https://cklixx.people.wm.edu/teaching/math400/Annette-paper.pdf