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	अनुक्रम-से-अनुक्रम मॉडल	अटेंशन मैकेनिज्म (Attention Mechanism)	रैंडम फ़ॉरेस्ट	बहु-शीर्षक स्व-ध्यान (Multi-Head Self-Attention)
क्षेत्र	गहन अधिगम	गहन अधिगम	मशीन अधिगम	गहन अधिगम
परिवार	Machine learning	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	2014	2015	2001	2017
प्रवर्तक	Sutskever, I.; Cho, K.	Bahdanau, D.; Luong, M.T.	Breiman, L.	Vaswani, A. et al.
प्रकार	Encoder-decoder neural network (deep learning)	Neural attention layer (encoder-decoder)	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
मौलिक स्रोत	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
उपनाम	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	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
संबंधित	5	5	4	5
सारांश	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.	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	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.