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	TextCNN	Jednostka bramkowana rekurencyjna (GRU)
Dziedzina	Uczenie głębokie	Uczenie głębokie
Rodzina	Machine learning	Machine learning
Rok powstania	2014	2014
Twórca	Kim, Y.	Cho, K. et al.
Typ	Convolutional neural network (deep learning)	Gated recurrent neural network unit
Źródło pierwotne	Kim, Y. (2014). Convolutional Neural Networks for Sentence Classification. EMNLP.	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.
Inne nazwy	CNN — Metin Sınıflandırma (TextCNN), convolutional neural network for sentence classification, sentence-level CNN, TextCNN	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network
Pokrewne	5	5
Podsumowanie	TextCNN is a convolutional neural network for text classification, introduced by Yoon Kim in 2014, that applies parallel convolution filters of different window sizes over word embeddings to capture local n-gram patterns. It is fast and effective for sentiment analysis and topic classification.	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.