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	Transformer (NLP)	XGBoost
Dziedzina	Uczenie głębokie	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2017	2016
Twórca	Vaswani, A. et al.	Chen, T. & Guestrin, C.
Typ	Attention-based deep neural network	Ensemble (gradient-boosted decision trees)
Źródło pierwotne	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Inne nazwy	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	XGBoost, extreme gradient boosting, scalable tree boosting
Pokrewne	4	5
Podsumowanie	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.