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	Word2Vec z samonadzorem	FastText	Rekurencyjna Sieć Neuronowa
Dziedzina	Uczenie głębokie	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2013	2016	1986–1990
Twórca	Mikolov, T., Chen, K., Corrado, G., & Dean, J.	Joulin, A.; Bojanowski, P.; Grave, E.; Mikolov, T. (Facebook AI Research)	Rumelhart, D. E.; Elman, J. L.
Typ	Self-supervised neural word embedding	Subword embedding model and linear text classifier	Sequential neural network
Źródło pierwotne	Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR 2013).	Joulin, A., Grave, E., Bojanowski, P. & Mikolov, T. (2017). Bag of Tricks for Efficient Text Classification. In Proceedings of EACL 2017, Short Papers, pp. 427–431. ACL.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
Inne nazwy	Word2Vec, word embeddings, Skip-gram model, CBOW model	fastText, fast text, subword embedding, character n-gram embedding	RNN, Elman network, Jordan network, simple recurrent network
Pokrewne	3	2	3
Podsumowanie	Word2Vec is a shallow neural network model introduced by Mikolov et al. (2013) that learns dense vector representations of words from large unlabeled text corpora using self-supervised objectives. By training a model to predict surrounding context words (Skip-gram) or a target word from its context (CBOW), it captures rich semantic and syntactic regularities in continuous vector space without any manual annotation.	FastText is a word embedding and text classification framework developed by Facebook AI Research (Joulin, Bojanowski, Grave, and Mikolov, 2016–2017) that represents each word as the sum of its character n-gram vectors, allowing it to construct meaningful representations for unseen and morphologically rich words and to perform near state-of-the-art text classification orders of magnitude faster than deep neural network alternatives.	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.