IMPROVING THE EFFICIENCY OF TRAINING ARTIFICIAL INTELLIGENCE MODELS USING NUMERICAL METHODS
DOI:
https://doi.org/10.55640/Keywords:
Artificial Intelligence, Numerical Methods, Neural Networks, Model Training, Optimization, UzbekistanAbstract
This study explores methods for improving the efficiency of training artificial intelligence (AI) models through numerical techniques. The research focuses on optimizing neural networks and sequential models using Gradient Descent, Stochastic Gradient Descent, and Runge–Kutta algorithms. Experimental results demonstrate that numerical approaches significantly enhance model accuracy, reduce error rates, and accelerate the training process. In the context of Uzbekistan, integrating AI models into digital education platforms enables personalized and interactive learning, reduces teachers’ workload, and improves student engagement and academic performance.Downloads
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.Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
4.Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv:1609.04747.
5.Chollet, F. (2018). Deep Learning with Python. Manning Publications.
6.Haykin, S. (2009). Neural Networks and Learning Machines. 3rd Edition. Pearson.
7.Nielsen, M. (2015). Neural Networks and Deep Learning. Determination Press.
8.Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in Neural Information Processing Systems, 27, 2672–2680.
9.O‘zbekiston Respublikasi Xalq ta’limi vazirligi. (2022). Raqamli ta’lim platformalari va innovatsiyalar bo‘yicha hisobot. Tashkent.
10.Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 265–283.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
Germany
United States of America
Italy
United Kingdom
France
Canada
Uzbekistan
Japan
Republic of Korea
Australia
Spain
Switzerland
Sweden
Netherlands
China
India
