পদ্ধতির তুলনা করুন

	ডাইলেটেড সিএনএন	XGBoost
ক্ষেত্র	গভীর শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2016	2016
প্রবর্তক	van den Oord, A. et al.; Bai, S., Kolter, J.Z. & Koltun, V.	Chen, T. & Guestrin, C.
ধরন	Deep learning (dilated 1D convolutional network)	Ensemble (gradient-boosted decision trees)
মৌলিক উৎস	van den Oord, A. et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
অপর নাম	Dilate Edilmiş CNN (WaveNet / TCN), WaveNet, Temporal Convolutional Network, TCN	XGBoost, extreme gradient boosting, scalable tree boosting
সম্পর্কিত	5	5
সারসংক্ষেপ	A Dilated CNN is a one-dimensional convolutional network whose receptive field grows exponentially with depth, letting it model long-range structure in time series and audio signals. WaveNet (van den Oord et al., 2016) and the Temporal Convolutional Network of Bai, Kolter and Koltun (2018) are the prominent members of this family.	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.