পদ্ধতির তুলনা করুন

	মাল্টিটাস্ক লার্নিং	ট্রান্সফার লার্নিং
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1997	2010 (formalized); 1990s (early roots)
প্রবর্তক	Rich Caruana	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
ধরন	Inductive transfer method	Learning paradigm
মৌলিক উৎস	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
অপর নাম	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme	TL, domain adaptation, fine-tuning, pre-trained model adaptation
সম্পর্কিত	3	3
সারসংক্ষেপ	Multitask Learning (MTL) is a machine learning paradigm in which a model is trained simultaneously on multiple related tasks, sharing representations across them to improve generalization. Introduced formally by Rich Caruana in 1997, MTL draws on the intuition that auxiliary tasks act as inductive bias, providing extra supervision signals that help the shared layers learn richer, more robust feature representations than single-task training would yield.	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.