Porovnat metody

	Autoencoder	Stroj s podpůrnými vektory (klasifikace)	Transformer (NLP)
Obor	Hluboké učení	Strojové učení	Hluboké učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2006	1995	2017
Tvůrce	Hinton, G.E. & Salakhutdinov, R.R.	Cortes, C. & Vapnik, V.	Vaswani, A. et al.
Typ	Neural network (encoder-decoder)	Maximum-margin classifier (kernel method)	Attention-based deep neural network
Původní zdroj	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Další názvy	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Příbuzné	4	5	4
Shrnutí	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	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.