পদ্ধতির তুলনা করুন

	নিউরাল আর্কিটেকচার সার্চ	ট্রান্সফার লার্নিং
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	2010 (formalized); 1990s (early roots)
প্রবর্তক	Zoph, B. & Le, Q.V.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Automated architecture optimization (deep learning)	Learning paradigm
মৌলিক উৎস	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
অপর নাম	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	5	3
সারসংক্ষেপ	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.