NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS
The research resulted in the development of software that implements various algorithms of neural networks optimization, which allowed to carry out their comparative analysis in terms of optimization quality. The article takes a detailed look at artificial neural networks and methods of their optimi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Science and Innovation Center Publishing House
2023-12-01
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| Series: | International Journal of Advanced Studies |
| Subjects: | |
| Online Access: | http://ijournal-as.com/jour/index.php/ijas/article/view/266 |
| _version_ | 1797300459746099200 |
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| author | Julia V. Torkunova Danila V. Milovanov |
| author_facet | Julia V. Torkunova Danila V. Milovanov |
| author_sort | Julia V. Torkunova |
| collection | DOAJ |
| description | The research resulted in the development of software that implements various algorithms of neural networks optimization, which allowed to carry out their comparative analysis in terms of optimization quality. The article takes a detailed look at artificial neural networks and methods of their optimization: quantization, overcutting, distillation, Tucker’s dissolution. Algorithms and optimization tools of neural networks were explained, as well as comparative analysis of different methods was conducted with their advantages and disadvantages listed. Calculation values were given as well as recommendations on how to execute each method. Optimization is studied by text classification performance: peculiarities were removed, models were chosen and taught, parameters were adjusted. The set task was completed with the use of the following technologies: Python programming language, Pytorch framework and Jupyter Notebook developing environment. The results that were acquired can be used to reduce the demand on computing power while preserving the same level of detection and classification abilities. |
| first_indexed | 2024-03-07T23:07:34Z |
| format | Article |
| id | doaj.art-e30c476906294626a6208a6c7cb4a913 |
| institution | Directory Open Access Journal |
| issn | 2328-1391 2227-930X |
| language | English |
| last_indexed | 2024-03-07T23:07:34Z |
| publishDate | 2023-12-01 |
| publisher | Science and Innovation Center Publishing House |
| record_format | Article |
| series | International Journal of Advanced Studies |
| spelling | doaj.art-e30c476906294626a6208a6c7cb4a9132024-02-22T01:32:07ZengScience and Innovation Center Publishing HouseInternational Journal of Advanced Studies2328-13912227-930X2023-12-0113414215810.12731/2227-930X-2023-13-4-142-158266NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSISJulia V. Torkunova0Danila V. Milovanov1Kazan State Power Engineering University; Sochi State UniversityKazan State Power Engineering UniversityThe research resulted in the development of software that implements various algorithms of neural networks optimization, which allowed to carry out their comparative analysis in terms of optimization quality. The article takes a detailed look at artificial neural networks and methods of their optimization: quantization, overcutting, distillation, Tucker’s dissolution. Algorithms and optimization tools of neural networks were explained, as well as comparative analysis of different methods was conducted with their advantages and disadvantages listed. Calculation values were given as well as recommendations on how to execute each method. Optimization is studied by text classification performance: peculiarities were removed, models were chosen and taught, parameters were adjusted. The set task was completed with the use of the following technologies: Python programming language, Pytorch framework and Jupyter Notebook developing environment. The results that were acquired can be used to reduce the demand on computing power while preserving the same level of detection and classification abilities.http://ijournal-as.com/jour/index.php/ijas/article/view/266artificial neural networksoptimizationcompression and accelerating of neural networkstext classificationquantizationtucker’s dissolutiondistillation |
| spellingShingle | Julia V. Torkunova Danila V. Milovanov NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS International Journal of Advanced Studies artificial neural networks optimization compression and accelerating of neural networks text classification quantization tucker’s dissolution distillation |
| title | NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS |
| title_full | NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS |
| title_fullStr | NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS |
| title_full_unstemmed | NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS |
| title_short | NEURAL NETWORKS OPTIMIZATION: METHODS AND THEIR COMPARISON BASED OFF TEXT INTELLECTUAL ANALYSIS |
| title_sort | neural networks optimization methods and their comparison based off text intellectual analysis |
| topic | artificial neural networks optimization compression and accelerating of neural networks text classification quantization tucker’s dissolution distillation |
| url | http://ijournal-as.com/jour/index.php/ijas/article/view/266 |
| work_keys_str_mv | AT juliavtorkunova neuralnetworksoptimizationmethodsandtheircomparisonbasedofftextintellectualanalysis AT danilavmilovanov neuralnetworksoptimizationmethodsandtheircomparisonbasedofftextintellectualanalysis |