2023
DOI: 10.3390/math11051183
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Modification of Learning Ratio and Drop-Out for Stochastic Gradient Descendant Algorithm

Abstract: The stochastic gradient descendant algorithm is one of the most popular neural network training algorithms. Many authors have contributed to modifying or adapting its shape and parametrizations in order to improve its performance. In this paper, the authors propose two modifications on this algorithm that can result in a better performance without increasing significantly the computational and time resources needed. The first one is a dynamic learning ratio depending on the network layer where it is applied, a… Show more

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