Сравнение методов

	Transformer (NLP)	Случайный лес
Область	Глубокое обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2017	2001
Автор метода	Vaswani, A. et al.	Breiman, L.
Тип	Attention-based deep neural network	Ensemble (bagging of decision trees)
Основополагающий источник	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	4	4
Сводка	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.	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.