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	Model sekwencyjny do sekwencyjnego (Seq2Seq)	Random Forest	Multi-Head Self-Attention
Dziedzina	Uczenie głębokie	Uczenie maszynowe	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2014	2001	2017
Twórca	Sutskever, I.; Cho, K.	Breiman, L.	Vaswani, A. et al.
Typ	Encoder-decoder neural network (deep learning)	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
Źródło pierwotne	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Inne nazwy	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention), öz-dikkat, multi-head attention, scaled dot-product attention
Pokrewne	5	4	5
Podsumowanie	The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.	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.	Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.