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	Gated Recurrent Unit (GRU)	XGBoost
Odbor	Hlboké učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2014	2016
Tvorca	Cho, K. et al.	Chen, T. & Guestrin, C.
Typ	Gated recurrent neural network unit	Ensemble (gradient-boosted decision trees)
Pôvodný zdroj	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Ďalšie názvy	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	XGBoost, extreme gradient boosting, scalable tree boosting
Príbuzné	5	5
Zhrnutie	The Gated Recurrent Unit (GRU) is a gated recurrent neural network cell introduced by Cho and colleagues in 2014 that captures long-range dependencies in sequential data using update and reset gates, achieving performance comparable to LSTM with fewer parameters.	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.