OPTIMIZATION PROCESS ANALYSIS FOR HYPERPARAMETERS OF NEURAL NETWORK DATA PROCESSING STRUCTURES
| Autor: | V. I. Solodovnikov, I. A. Evdokimov, V. N. Gridin, B. R. Salem |
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| Rok vydání: | 2020 |
| Předmět: |
Hyperparameter
Data processing Artificial neural network business.industry Computer science Process analysis Computer Science::Neural and Evolutionary Computation 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications Pattern recognition 02 engineering and technology Artificial intelligence business |
| Zdroj: | Vestnik komp'iuternykh i informatsionnykh tekhnologii. :3-10 |
| ISSN: | 1810-7206 |
| Popis: | The analysis of key stages, implementation features and functioning principles of the neural networks, including deep neural networks, has been carried out. The problems of choosing the number of hidden elements, methods for the internal topology selection and setting parameters are considered. It is shown that in the training and validation process it is possible to control the capacity of a neural network and evaluate the qualitative characteristics of the constructed model. The issues of construction processes automation and hyperparameters optimization of the neural network structures are considered depending on the user's tasks and the available source data. A number of approaches based on the use of probabilistic programming, evolutionary algorithms, and recurrent neural networks are presented. |
| Databáze: | OpenAIRE |
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