Advanced methods for optimizing work with neural networks on modern architectures
Journal Title: Modern Innovations, Systems and Technologies - Year 2024, Vol 4, Issue 4
Abstract
The paper is devoted to the study and comparison of different optimization methods for training neural networks. Key algorithms such as stochastic gradient descent, Momentum method, AdaGrad and Adam are considered. For each method, theoretical justifications, mathematical formulas and examples of practical implementation are provided. An experimental comparison of the performance of these methods on the task of handwritten digit classification using the MNIST dataset is carried out. The advantages and disadvantages of each method are discussed, as well as their impact on the learning rate and accuracy of the model. Based on the results obtained, the most efficient algorithm is summarized, and the importance of selecting an appropriate optimization method to improve the efficiency of neural networks in various applications is emphasized.
Authors and Affiliations
P. M. Urvachev, V. A. Kovtun
Keywords
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- EP ID EP755369
- DOI 10.47813/2782-2818-2024-4-4-0199-0212
- Views 27
- Downloads 0
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