قارن الطرق

	الشبكة العصبية التلافيفية (التصنيف)	XGBoost
المجال	التعلم العميق	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	1998	2016
صاحب الطريقة	LeCun, Y. et al.	Chen, T. & Guestrin, C.
النوع	Deep neural network (convolutional)	Ensemble (gradient-boosted decision trees)
المصدر التأسيسي	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
الأسماء البديلة	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	XGBoost, extreme gradient boosting, scalable tree boosting
ذات صلة	5	5
الملخص	A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.