TASVIRLARNI TANISHDA NEYRON TARMOQLARINING MODELI, ALGORITMI VA DASTURIY VOSITALARINI KO’PYADROLI PROSESSORLAR MUHITIDA ISHLAB CHIQISH
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Аннотация:
So'nggi o'n yilliklarda sun'iy intellekt va chuqur o'rganish sohalarida amalga oshirilgan tadqiqotlar natijasida tasvirlarni tanish texnologiyalarida sezilarli yutuqlar qo'lga kiritildi. Neyron tarmoqlarining aniqlik va samaradorligi tufayli ular ko'plab amaliy sohalarda, jumladan, tibbiyot, transport, xavfsizlik va ko'ngilochar sohalarda keng qo'llanilmoqda. Ushbu maqolada ko'pyadroli protsessorlar muhitida tasvirlarni tanish uchun neyron tarmoqlarning modeli, algoritmi va dasturiy vositalarini ishlab chiqish masalalari batafsil ko'rib chiqiladi.
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LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536.
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., ... & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (pp. 265-283).
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., ... & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 8024-8035.
Chollet, F. (2015). Keras: The Python deep learning library. Astrophysics Source Code Library