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	Rekurencyjna Sieć Neuronowa	XGBoost
Dziedzina	Uczenie głębokie	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1986–1990	2016
Twórca	Rumelhart, D. E.; Elman, J. L.	Chen, T. & Guestrin, C.
Typ	Sequential neural network	Ensemble (gradient-boosted decision trees)
Źródło pierwotne	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Inne nazwy	RNN, Elman network, Jordan network, simple recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
Pokrewne	3	5
Podsumowanie	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	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.