Rekurrent (RNN) va svyortkali (CNN) neyron tarmoqlar
Annotatsiya
Svyortkali Neyron Tarmoqlar (CNN) va Rekurrent Neyron Tarmoqlar (RNN) zamonaviy chuqur o'rganishning ikki asosiy yo'nalishini tashkil etadi. CNN fazoviy ma'lumotlarni ierarxik tarzda qayta ishlash orqali kompyuter ko'rishi sohasini tubdan o'zgartirdi. RNN esa LSTM va GRU kabi kengaytirilgan variantlari bilan ketma-ket ma'lumotlardagi vaqtinchalik bog'liqliklarni samarali modellashtiradi. Ushbu maqola har ikki arxitekturaning nazariy asoslari, matematik ifodalari, o'qitish usullari va cheklovlarini qamrab oladi. Shuningdek, taqqosiy tahlil, amaliy qo'llanishlar, gibrid yondashuvlar va kelajak tadqiqot istiqbollari ham muhokama qilinadi..
Kalit so‘zlar: Konvolyutsion Neyron Tarmoqlar, Rekurrent Neyron Tarmoqlar, LSTM, GRU, Chuqur O‘rganish, Kompyuter Ko‘rishi.
Библиографические ссылки
1. Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.
2. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. EMNLP 2014.
3. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
4. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation.
5. 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 (CVPR).
6. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems.
7. Abdukadirov, B. A., & Rahmonaliyeva, M. (2025). Rekurrent neyron tarmoqlar (RNN) va ularning qo‘llanilishi. Ilm-fan va innovatsiyalar.
