QAT'IYMAS NEYRON TARMOQLARI
DOI:
https://doi.org/10.5281/zenodo.11279764Keywords:
Qat'iymas neyron tarmoqlari, sun'iy intellekt, o'rganish algoritmlari, rasmlarni tanib olish, tabiiy tilni qayta ishlash, o'yinlar, avtonom transport, optimallashtirish usullari, etik masalalar, kelajak istiqbollari, shaffoflik, ma'lumotlar maxfiyligi, energiya samaradorligi, global muammolar, texnologik yutuqlar.Abstract
Ushbu maqola, sun'iy intellekt sohasida qat'iymas neyron tarmoqlari va ularning xulosa qoidalari haqida batafsil ma'lumot beradi. Maqolada qat'iymas neyron tarmoqlarining asosiy tuzilishi, o'rganish jarayonlari va ularning turli sohalardagi amaliy qo'llanilishlari yoritiladi. Shuningdek, qat'iymas neyron tarmoqlarining kelajakdagi rivojlanish istiqbollari va yangi tadqiqot yo'nalishlari ham ko'rib chiqiladi. Maqola, qat'iymas neyron tarmoqlarining nazariy asoslari va amaliyotdagi ahamiyatini chuqur tahlil qilish bilan birga, ushbu texnologiyalarning kelajakdagi rivojlanish yo'nalishlarini ham ta'kidlaydi.
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.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. *Advances in neural information processing systems*, 25.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. *Neural computation*, 9(8), 1735-1780.
Vaswani, A., et al. (2017). Attention is all you need. *Advances in neural information processing systems*, 30.
Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. *Nature*, 529(7587), 484-489.
Bojarski, M., et al. (2016). End to End Learning for Self-Driving Cars. *arXiv preprint arXiv:1604.07316*.
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. *International Conference on Medical image computing and computer-assisted intervention*.
Bello, I., et al. (2016). Neural combinatorial optimization with reinforcement learning. *arXiv preprint arXiv:1611.09940*.
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. *arXiv preprint arXiv:1412.6980*.
Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. *European conference on computer vision*.
Szegedy, C., et al. (2015). Going deeper with convolutions. *Proceedings of the IEEE conference on computer vision and pattern recognition*.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. *Proceedings of the IEEE conference on computer vision and pattern recognition*.
Abadi, M., et al. (2016). TensorFlow: A system for large-scale machine learning. *12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16)*.
Chollet, F. (2017). *Deep Learning with Python*. Manning Publications.
