SUN’IY INTELLEKTLARDA STOXASTIK O‘QITISH USULLARINING ROLI VA AFZALLIKLARI
Keywords:
sun’iy intellekt, stoxastik o‘qitish, stoxastik gradient tushish, neyron tarmoq, mashina o‘qitish, optimallashtirish, ehtimollik usullari.Abstract
Ushbu maqolada sun’iy intellekt tizimlarida stoxastik o‘qitish usullarining mohiyati, ularning afzalliklari va amaliy qo‘llanilish sohasi tahlil qilinadi. Stoxastik o‘qitish algoritmlari, xususan, stoxastik gradient tushish (SGD) usuli, neyron tarmoqlarning o‘qitilishida muhim ahamiyat kasb etadi. Maqolada deterministik va stoxastik yondashuvlarning farqlari ko‘rsatilib, stoxastik usullarning modelni tezroq va barqaror o‘qitishdagi roli yoritiladi. Shuningdek, ushbu usullar yordamida sun’iy intellekt tizimlarining umumlashtirish qobiliyatini oshirish imkoniyatlari ham ko‘rib chiqiladi.
References
1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
3. Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. Proceedings of COMPSTAT.
4. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow. O’Reilly Media.
5. Haykin, S. (2009). Neural Networks and Learning Machines. Pearson Education.
6. Muhammadjonov A., Karimov S. (2021). Sun’iy intellekt asoslari. Toshkent: “Fan va texnologiya” nashriyoti.
7. Abduvaliyev O. (2022). Mashinali o‘qitishning stoxastik usullari. Toshkent axborot texnologiyalari universiteti ilmiy to‘plami.
8. Kingma, D. P., & Ba, J. (2015). Adam: A Method for Stochastic Optimization. International Conference on Learning Representations (ICLR).
