NAMUNALARNI ANIQLASH MASALALARI
Ключевые слова:
mashinaviy o'rganish, chuqur o'rganish, namuna aniqlash, qo'llab-quvvatlash vektor mashinalari, qaror daraxtlari, sun'iy neyron tarmoqlari, yuzni aniqlash, yuzni tan olish, noto'g'ri ma'lumotlar, ma'lumotlarni tozalash.Аннотация
Ushbu maqola mashinaviy o'rganish va chuqur o'rganish sohalarida namunalarni aniqlash masalalari va ularning hal etilish usullariga bag'ishlangan. Maqolada namunalarni aniqlashning uchta asosiy algoritmi: qo'llab-quvvatlash vektor mashinalari (SVM), qaror daraxtlari va sun'iy neyron tarmoqlari, shuningdek chuqur o'rganish texnologiyalari yordamida yuzni aniqlash va tan olish, noto'g'ri ma'lumotlarni tozalash va ularning namuna tan olishdagi ta'siri kabi mavzular qamrab olingan. Har bir usulning afzalliklari va cheklovlari, shuningdek ularning amaliy qo'llanilish sohalariga alohida e'tibor qaratilgan.
Библиографические ссылки
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems.
Russakovsky, O., Deng, J., Su, H., et al. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision.
Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press.
Zhang, C., & Ma, Y. (2020). Machine Learning. Tsinghua University Press.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.2. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.4. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Holland, J. H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press.6. Bishop, C. M. (2006). Pattern recognition and machine learning. springer.
Nurmamatovich, T. I. (2024, April). BIR QATLAMLI PERCEPTRONNI O ‘QITISH. In " CANADA" INTERNATİONAL CONFERENCE ON DEVELOPMENTS İN EDUCATİON, SCİENCESAND HUMANİTİES (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024, April). SUN'IY NEYRONNING MATEMATIK MODELI HAMDA FAOLLASHTIRISH FUNKTSIYALARI. In " USA" INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TOPICAL ISSUES OF SCIENCE (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024, April). SUNIY NEYRON TORLARINI ADAPTIV KUCHAYTIRISH USULI. In " USA" INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TOPICAL ISSUES OF SCIENCE (Vol. 17, No. 1).
Nurmamatovich, T. I. (2024, April). SUNIY NEYRON TORLARINI ADAPTIV KUCHAYTIRISH USULI. In " USA" INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE TOPICAL ISSUES OF SCIENCE (Vol. 17, No. 1).
Tojimamatov, I. N., Olimov, A. F., Khaydarova, O. T., & Tojiboyev, M. M. (2023). CREATING A DATA SCIENCE ROADMAP AND ANALYSIS. PEDAGOGICAL SCIENCES AND TEACHING METHODS, 2(23), 242-250.
Тожимаматов, И. Н. (2023). ЗАДАЧИ ИНТЕЛЛЕКТУАЛЬНОГО АНАЛИЗА ДАННЫХ. PEDAGOG, 6(4), 514-516.
Muqaddam, A., Shahzoda, A., Gulasal, T., & Isroil, T. (2023). NEYRON TARMOQLARDAN FOYDALANIB TASVIRLARNI ANIQLASH USULLARI. SUSTAINABILITY OF EDUCATION, SOCIO-ECONOMIC SCIENCE THEORY, 1(8), 63-74.
Raximov, Q. O., Tojimamatov, I. N., & Xo, H. R. O. G. L. (2023). SUNIY NЕYRON TARMOQLARNI UMUMIY TASNIFI. Scientific progress, 4(5), 99-107.
Ortiqovich, Q. R., & Nurmamatovich, T. I. (2023). NEYRON TARMOQNI O ‘QITISH USULLARI VA ALGORITMLARI. Scientific Impulse, 1(10), 37-46.
Tojimamatov, I. N., Mamalatipov, O., Rahmatjonov, M., & Farhodjonov, S. (2023). NEYRON TARMOQLAR. Наука и инновация, 1(1), 4-12.
Tojimamatov, I. N., Mamalatipov, O. M., & Karimova, N. A. (2022). SUN’IY NEYRON TARMOQLARINI O ‘QITISH USULLARI. Oriental renaissance: Innovative, educational, natural and social sciences, 2(12), 191-203.
Muqaddam, A., Shahzoda, A., Gulasal, T., & Isroil, T. (2023). NEYRON TARMOQLARDAN FOYDALANIB TASVIRLARNI ANIQLASH USULLARI. SUSTAINABILITY OF EDUCATION, SOCIO-ECONOMIC SCIENCE THEORY, 1(8), 63-74.
Raximov, Q. O., Tojimamatov, I. N., & Xo, H. R. O. G. L. (2023). SUNIY NЕYRON TARMOQLARNI UMUMIY TASNIFI. Scientific progress, 4(5), 99-107.
Raxmatjonova, M. N., & Tojimamatov, I. N. (2023). BIZNESDA SUNIY INTELEKT TEXNOLOGYALARI VA ULARNI AHAMIYATI. Лучшие интеллектуальные исследования, 11(3), 46-52.
Nurmamatovich, T. I. (2024). NORMALLASHTIRISH. NORMAL FORMALAR. worldly knowledge conferens, 7(2), 597-599.
Nurmatovich, T. I. (2024). Bir qatlamli va ko ‘p qatlamli neyron to ‘rlari. ILM FAN XABARNOMASI, 1(1), 190-191.
Tojimamatov, I., & G’ulomjonova, S. (2024). NEYRO KOMPYUTERLAR VA ULARNING ARXITEKTURASI. Development of pedagogical technologies in modern sciences, 3(6), 10-16.
Raximov, Q. O., & qizi Kuchkarova, M. R. (2023). SUN’IY INTELLEKTNI RADIOLOGIYADA QO ‘LLASH MODELLARI VA TASVIRLARNI O ‘QITISH MASALALARI. GOLDEN BRAIN, 1(17), 397-400.
