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Данная работа посвящена исследования внешних условий, влияющих на процесс обучения модели нейронной сети, успешность обучения, изменения метрик обучения модели. Совокупность подходов обучения и подготовки данных позволили выявить эффективные функции активации на определенных наборах данных при задаче классификации.
This work is devoted to the study of external conditions that affect the learning process of a neural network model, the success of training, and changes in the model's learning metrics. A combination of training and data preparation approaches made it possible to identify effective activation functions on certain data sets in the classification task.
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