NEYROKOMPYUTERLAR ARXITEKTURASI
Abstract
Maqolada "Neyrokompyuterlar Arxitekturasi" mavzusi yoritilgan bo'lib, unda neyrokompyuterlar — biologik neyron tarmoqlari asosida qurilgan va sun'iy intellektni rivojlantirishda qo'llaniladigan kompyuter tizimlarining arxitekturasi va ishlash prinsiplari haqida so'z yuritiladi. Maqolada neyrokompyuterlarning qurilishi, ularning ishlash algoritmlari, o'qitish jarayoni, shuningdek, ma'lumotlarni qayta ishlash va qaror qabul qilishda qanday samarali ishlashini ko'rib chiqishadi. Shuningdek, neyrokompyuterlarning turli sohalarda, jumladan, tasvirni tanish, nutqni qayta ishlash, tibbiyot va robototexnika kabi sohalarda qo'llanilishining afzalliklari va qiyinchiliklari tahlil qilinadi.Maqola, neyrokompyuterlar arxitekturasining zamonaviy tendensiyalarini o'rganish va bu sohadagi ilg'or tadqiqotlar va amaliy qo'llanilishini tushunishga yordam beradi.
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Nielsen, M. (2015). Neural Networks and Deep Learning. Determination Press.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS).
Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504-507. 8.Bengio, Y. (2012). Learning Deep Architectures for AI. Foundations and Trends in Machine Learning,2(1),1-127. 9.Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv:1511.06434.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. Advances in Neural Information Processing Systems (NeurIPS).
Silver, D., Hubert, T., Schrittwieser, J., et al. (2020). A General Reinforcement Learning Algorithm that Masters Chess, Shogi, and Go through Self-Play. Science, 362(6419), 1140-1144.
Yuan, Y., & Yang, Q. (2019). Artificial Intelligence and Machine Learning: A Survey. Springer.
