Porovnat metody

	Konvoluční neuronová síť (klasifikace)	Autoencoder	Transformer (NLP)
Obor	Hluboké učení	Hluboké učení	Hluboké učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1998	2006	2017
Tvůrce	LeCun, Y. et al.	Hinton, G.E. & Salakhutdinov, R.R.	Vaswani, A. et al.
Typ	Deep neural network (convolutional)	Neural network (encoder-decoder)	Attention-based deep neural network
Původní zdroj	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Další názvy	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Příbuzné	5	4	4
Shrnutí	A Convolutional Neural Network (CNN) is a deep learning model, established by LeCun and colleagues in 1998, that learns local patterns directly from images and structured data to classify them. Stacks of convolutional filters discover increasingly abstract features, so manual feature engineering can be largely reduced.	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 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.