পদ্ধতির তুলনা করুন

	অ্যাটেনশন মেকানিজম	Random Forest	মাল্টি-হেড সেলফ-অ্যাটেনশন
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2015	2001	2017
প্রবর্তক	Bahdanau, D.; Luong, M.T.	Breiman, L.	Vaswani, A. et al.
ধরন	Neural attention layer (encoder-decoder)	Ensemble (bagging of decision trees)	Attention mechanism (Transformer core)
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	5	4	5
সারসংক্ষেপ	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.