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www.scientificprogress.uzBog'liq sun-iy-neyron-tarmoqlarni-umumiy-tasnifi HamdamovAyyubxon, ma\'lumotlar tuzulmasi va algaridimlar amaliy ish - 1, Ma\'lumotlar bazasi mustaqil ish -3, Ma\'lumotlarni saralash algoritmlari. Saralash tushunchasi va uni-kompy.info, Mashinali o\'qitishga kirish 21-ma\'ruza Nosirov Kh, Mashinali o\'qitishga kirish 1-ma\'ruza Nosirov Kh (1), Asosnoma Quraqov S.A., Haydarqulov Shohzod, OR-5.51.02.02-Elektr atansiyalari tarmoqlari va tizimlari, lab5power point, Kompyuter tarmoqlari va adminstratorlash fanidan test savollari , 8-sinf mavzulashtirilgan testlar, menejment texwww.scientificprogress.uz
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ma’lumotlat
bazasining
konseptual
sxemasini
aniqlovchi
mutaxassislarga
yordamlashishda; kompyuter tizimlarida - lokal tizimlami loyihalashda va katta
EHMdagi katta razryadli MVT operatsion tizimlami boshqarishda; elektronikada -
telefon tarmog‘idagi nosozliklarni aniqlashda, uni sozlash va tiklash chora-tadbirlari
bo‘yicha tavsiyalar berishda; energetikada — energetik tizimlarda ishdan chiqish
holatlarim aniqlash va tuzatishda; geologiyada - foydali qazilmalami topishda va
holatini aniqlashda; qishloq xo‘jaligida - mevali bog‘larga qarashga maslahat berishda;
matematikada — teoremalarni isbotlashda va algebraik ifodalami soddalashtirishda;
kimyoda — murakkab organik molekulalar strukturalarini anglashda; biologiyada —
DNK strukturasini aniqlashda keng va samarali tadbiq etilmoqda.
Foydalanilgan adabiyotlar:
1. Jordan, J. Intro to optimization in deep learning: Gradient Descent/ J. Jordan // Paper-space.
Series: Optimization. – 2018. – URL: https://blog.paperspace.com/intro-to-optimiza-tion-in-
deep-learning-gradient-descent/
2. Scikit-learn – машинное обучение на Python. – URL: http://scikit-learn.org/stable/
modules/generated/sklearn.neural_network. MLPClassifier.html
3. Keras documentation: optimizers. – URL: https://keras.io/optimizers
4. Ruder, S. An overview of gradient descent optimization algorithms / S. Ruder // Cornell
University Library. – 2016. – URL: https://arxiv. org/abs/1609.04747
5. Robbins, H. A stochastic approximation method / H. Robbins, S. Monro // The annals of
mathematical statistics. – 1951. – Vol. 22. – P. 400–407.
6. Kukar, M. Cost-Sensitive Learning with Neural Networks / M. Kukar, I. Kononenko //
Machine Learning and Data Mining : proceed-ings of the 13th European Conference on Artifi-
cial Intelligence. – 1998. – P. 445–449.
7. Duchi, J. Adaptive Subgradient Methods for Online Learning and Stochastic Optimiza-tion /
J. Duchi, E. Hazan, Y. Singer // The Jour-nal of Machine Learning Research. – 2011. – Vol.
12. – P. 2121–2159.
8. Zeiler, M. D. ADADELTA: An Adap-tive Learning Rate Method / Cornell Univer-sity
Library. – 2012. – URL: https://arxiv.org/ abs/1212.5701
9. Kingma, D. P. Adam: A Method for Sto-chastic Optimization / D. P. Kingma, J. Ba // Cor-
nell University Library. – 2014. – URL: https:// arxiv.org/abs/1412.6980
10. Гудфеллоу, Я. Глубокое обучение / Я. Гу-дфеллоу, И. Бенджио, А. Курвилль. – М. :
ДМК Пресс, 2018. – 652 с.
11. Fletcher, R. Practical methods of optimi-zation / R. Fletcher. – Wiley, 2000. – 450 p.
12. Schraudolph, N. N. A Stochastic Qua-si-Newton Method for Online Convex Optimiza-tion
/ N.N. Schraudolph, J. Yu, S. Gunter // Sta-tistical Machine Learning. – 2017. – URL: http://
proceedings.mlr.press/v2/schraudolph07a/ schraudolph07a.pdf
SCIENTIFIC PROGRESS
VOLUME 4 ǀ ISSUE 5 ǀ 2023
ISSN: 2181-1601
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