পদ্ধতির তুলনা করুন

	কারিকুলাম লার্নিং	মাল্টিটাস্ক লার্নিং
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2009	1997
প্রবর্তক	Yoshua Bengio et al.	Rich Caruana
ধরন	Training strategy	Inductive transfer method
মৌলিক উৎস	Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009). Curriculum learning. International Conference on Machine Learning (ICML), 41–48.	Caruana, R. (1997). Multitask learning. Machine Learning, 28(1), 41–75.
অপর নাম	Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme
সম্পর্কিত	3	3
সারসংক্ষেপ	Curriculum Learning is a training strategy for machine learning models, introduced by Bengio et al. in 2009, in which training examples are presented in a meaningful order—typically from easy to hard—rather than at random. Inspired by how humans and animals learn progressively, it organizes training data into a curriculum that starts with simpler, cleaner, or more representative samples and gradually introduces harder or more complex examples as the model matures.	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.