Linganisha mbinu

	RNN ya pande mbili	Kitengo cha Kurudiana kilicho na Lango (GRU)	Msitu Nasibu	Muundo wa Mfuatano-hadi-Mfuatano
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1997	2014	2001	2014
Mwanzilishi	Schuster, M. & Paliwal, K.K.	Cho, K. et al.	Breiman, L.	Sutskever, I.; Cho, K.
Aina	Recurrent neural network (sequence model)	Gated recurrent neural network unit	Ensemble (bagging of decision trees)	Encoder-decoder neural network (deep learning)
Chanzo asilia	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Cho, K. et al. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Majina mbadala	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	Kapılı Tekrarlayan Birim (GRU), gated recurrent unit, gated recurrent network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Zinazohusiana	5	5	4	5
Muhtasari	A Bidirectional RNN, introduced by Schuster and Paliwal in 1997, processes a sequence in both forward and backward directions so that every position has access to its full surrounding context. With LSTM or GRU cells (BiLSTM/BiGRU) it is the standard approach for named-entity recognition, sequence labelling, and speech recognition.	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.	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.	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.