Keywords
Abstract
Ushbu maqolada sun’iy neyron tarmoqlarning nazariy asoslari, tuzilishi, oʻqitish jarayoni, asosiy turlari va amaliy qoʻllanilish sohalari ilmiy nuqtai nazardan koʻrib chiqiladi. Maqolada neyron, perceptron, aktivatsiya funksiyalari, tarmoq arxitekturasi, backpropagation algoritmi va gradient tushish usullari batafsil yoritilgan. Bundan tashqari, ANN, CNN va RNN kabi asosiy neyron tarmoq turlari tahlil qilinib, ularning tibbiyot, ta’lim, biznes, axborot xavfsizligi va sun’iy intellekt tizimlaridagi oʻrni muhokama qilingan.
References
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. ISBN: 978-0262035613. URL: https://www.deeplearningbook.org
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
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 (NeurIPS), 30. arXiv:1706.03762.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR 2016. arXiv:1512.03385.
Chollet, F. (2021). Deep Learning with Python (2nd ed.). Manning Publications. ISBN: 978-1617296864.
Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press. URL: http://neuralnetworksanddeeplearning.com
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072.
Haykin, S. (2009). Neural Networks and Learning Machines (3rd ed.). Pearson. ISBN: 978-0131471399.