Сравнение на методи

	SGD с Momentum / Adam Optimizer	Нормализация по мини-партиди (Batch Normalization)
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2015	2015
Създател	Rumelhart, Hinton & Williams (momentum SGD, 1986); Kingma & Ba (Adam, 2015)	Ioffe, S. & Szegedy, C.
Тип	First-order adaptive stochastic optimizer	Normalization technique (applied per mini-batch during training)
Основополагащ източник	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.
Други названия	Adam, Adam optimizer, SGD with momentum, momentum SGD	BatchNorm, BN, batch norm, mini-batch normalization
Свързани	1	1
Резюме	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.