Salīdzināt metodes

	Mācīšanās pēc programmas	Daudzuzdevumu apguve
Nozare	Dziļā mācīšanās	Dziļā mācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	1997
Autors	Yoshua Bengio et al.	Rich Caruana
Tips	Training strategy	Inductive transfer method
Pirmavots	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.
Citi nosaukumi	Scheduled Training, Difficulty-Based Training, Self-Paced Learning, Müfredat Öğrenimi	MTL, Joint Learning, Shared Representation Learning, Çok Görevli Öğrenme
Saistītās	3	3
Kopsavilkums	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.