Porovnat metody

	Transformer (NLP)	Autoencoder	Logistická regrese
Obor	Hluboké učení	Hluboké učení	Statistika ve výzkumu
Rodina	Machine learning	Machine learning	Process / pipeline
Rok vzniku	2017	2006	1958
Tvůrce	Vaswani, A. et al.	Hinton, G.E. & Salakhutdinov, R.R.	David Roxbee Cox
Typ	Attention-based deep neural network	Neural network (encoder-decoder)	Method
Původní zdroj	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Další názvy	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	logit model, binomial logistic regression, LR
Příbuzné	4	4	3
Shrnutí	The Transformer is an attention-based deep learning model, introduced by Vaswani and colleagues in 2017, that performs text classification, named-entity recognition, and language modelling by letting every token in a sequence attend directly to every other token. It replaced earlier recurrent designs with a self-attention mechanism that processes whole sequences in parallel.	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.