NEYRON TO‘RDA CHIQISH QATLAMINING TUZILISHI VA VAZIFALARI
Keywords:
Kalit so‘zlar: sun’iy neyron tòr, chiqish qatlami, aktivatsiya funksiyasi, sigmoid, softmax, regressiya, tasniflash.Abstract
Annotatsiya: Ushbu maqolada sun’iy neyron tòrlar tarkibidagi chiqish
qatlamining tuzilishi va asosiy vazifalari batafsil yoritilgan. Chiqish qatlamining
neyronlar soni, aktivatsiya funksiyalarining tanlanishi hamda ularning tasniflash va
regressiya masalalaridagi roli ko‘rib chiqilgan. Shuningdek, chiqish qatlamining model
natijasini shakllantirishdagi ahamiyati va yo‘qotish funksiyasi bilan bog‘liqligi
tushuntirilgan. Maqola sun’iy intellekt va mashinaviy o‘rganish sohasida ta’lim
olayotgan talabalar uchun foydali manba bo‘lib xizmat qiladi.
References
FOYDALANILGAN ADABIYOTLAR
1. Goodfellow I., Bengio Y., Courville A. Deep Learning. — MIT Press, 2016.
2. Haykin S. Neural Networks and Learning Machines. — Pearson, 3rd Edition, 2009.
3. Bishop C. M. Pattern Recognition and Machine Learning. — Springer, 2006.
4. Russell S., Norvig P. Artificial Intelligence: A Modern Approach. — Pearson, 4th
Edition, 2020.
5. Aggarwal C. C. Neural Networks and Deep Learning. — Springer, 2018.
6. Mitchell T. M. Machine Learning. — McGraw-Hill, 1997.
7. LeCun Y., Bengio Y., Hinton G. “Deep Learning”, Nature, vol. 521, pp. 436–444,
2015.
8. Schmidhuber J. Deep Learning in Neural Networks: An Overview, Neural
Networks, 2015, vol. 61, pp. 85–117.
9. Nielsen M. A. Neural Networks and Deep Learning. — Determination Press, 2015.
10. He K., Zhang X., Ren S., Sun J. “Deep Residual Learning for Image Recognition”,
CVPR, 2016.
11. Chollet F. Deep Learning with Python. — Manning Publications, 2017.
12. Goodfellow I., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S.,
Courville A., Bengio Y. “Generative Adversarial Nets”, NeurIPS, 2014.
13. Hinton G., Osindero S., Teh Y. W. “A Fast Learning Algorithm for Deep Belief
Nets”, Neural Computation, 2006, vol. 18, pp. 1527–155