DELTA QOIDASI USULI VA DIFFERENTSIALLASHNING ZANJIRLI QOIDASI
Keywords:
Sun'iy Intellekt, Neyron Tarmoqlari, Delta Qoidasi, Gradient Descent, Differentsiallashning Zanjirli Qoidasi, Orqaga Tarqatish, Optimallashtirish, Xatolik Funktsiyasi, O'rganish Tezligi.Abstract
Ushbu maqola sun'iy intellekt sohasida, xususan neyron tarmoqlarini optimallashtirish va o'qitish jarayonlarida qo'llaniladigan ikkita muhim usul — Delta qoidasi va differentsiallashning zanjirli qoidasi haqida batafsil ma'lumot beradi. Delta qoidasi, neyron tarmoqlaridagi og'irligini yangilash usuli sifatida ta'riflanib, uning xatolik funktsiyasi va gradient descent yordamida qanday ishlashi bayon qilingan. Differentsiallashning zanjirli qoidasi esa, murakkab funksiyalarning hosilalarini hisoblashda qo'llaniladi va neyron tarmoqlarida orqaga tarqatish jarayonida gradientlarni aniq hisoblash imkonini beradi. Maqola, ushbu konseptlarning texnik jihatlarini chuqur tushuntirish bilan birga, amaliy qo'llanilishlarini ham ko'rsatib beradi.
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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, 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). XEBB O’QITISH QOIDASI. " GERMANY" MODERN SCIENTIFIC RESEARCH: ACHIEVEMENTS, INNOVATIONS AND DEVELOPMENT PROSPECTS, 17(1).
Nurmamatovich, T. I., & Azizjon o’g, N. A. Z. (2024). Neural network clustering methods. American Journal of Open University Education, 1(1), 16-18.
