เปรียบเทียบวิธี

	SGD with Momentum / Adam Optimizer	Batch Normalization
สาขาวิชา	การเรียนรู้เชิงลึก	การเรียนรู้เชิงลึก
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2015	2015
ผู้ริเริ่ม	Rumelhart, Hinton & Williams (momentum SGD, 1986); Kingma & Ba (Adam, 2015)	Ioffe, S. & Szegedy, C.
ประเภท	First-order adaptive stochastic optimizer	Normalization technique (applied per mini-batch during training)
แหล่งต้นตำรับ	Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. International Conference on Learning Representations (ICLR 2015). arXiv:1412.6980.	Ioffe, S. & Szegedy, C. (2015). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning (ICML), PMLR 37, 448–456.
ชื่อเรียกอื่น	Adam, Adam optimizer, SGD with momentum, momentum SGD	BatchNorm, BN, batch norm, mini-batch normalization
ที่เกี่ยวข้อง	1	1
สรุป	Stochastic Gradient Descent (SGD) with momentum and its adaptive descendant Adam are the foundational parameter-update algorithms used to train virtually every modern deep learning model. Momentum SGD was formalised by Polyak (1964) and brought into neural network training by Rumelhart, Hinton, and Williams (1986). Adam, introduced by Kingma and Ba at ICLR 2015, extended the momentum idea by also maintaining a running average of squared gradients, producing per-parameter adaptive learning rates that make it the default optimizer in contemporary deep learning practice.	Batch Normalization is a training technique introduced by Sergey Ioffe and Christian Szegedy in 2015 that normalizes the pre-activation outputs of each layer using the mean and variance computed over the current mini-batch. By stabilizing the input distribution to each layer throughout training, it substantially reduces internal covariate shift, enabling the use of higher learning rates and making deep networks train faster and more reliably.