Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Konvolučná neurónová sieť (klasifikácia)× | Náhodný les× | Transformer (NLP)× | |
|---|---|---|---|
| Odbor≠ | Hlboké učenie | Strojové učenie | Hlboké učenie |
| Rodina | Machine learning | Machine learning | Machine learning |
| Rok vzniku≠ | 1998 | 2001 | 2017 |
| Tvorca≠ | LeCun, Y. et al. | Breiman, L. | Vaswani, A. et al. |
| Typ≠ | Deep neural network (convolutional) | Ensemble (bagging of decision trees) | Attention-based deep neural network |
| Pôvodný zdroj≠ | LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ | Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS. link ↗ |
| Ďalšie názvy | CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble | Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP |
| Príbuzné≠ | 5 | 4 | 4 |
| Zhrnutie≠ | A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. | The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel. |
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