Linganisha mbinu

	RNN ya pande mbili	Msitu Nasibu	Uzingatio-mkuu wa nafsi (Multi-Head Self-Attention)
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Mashine	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning	Machine learning
Mwaka wa asili	1997	2001	2017
Mwanzilishi	Schuster, M. & Paliwal, K.K.	Breiman, L.	Vaswani, A. et al.
Aina	Recurrent neural network (sequence model)	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
Chanzo asilia	Schuster, M. & Paliwal, K.K. (1997). Bidirectional Recurrent Neural Networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Majina mbadala	Çift Yönlü RNN / BiLSTM / BiGRU, bidirectional recurrent neural network, BiLSTM, BiGRU	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
Zinazohusiana	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.	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.