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	LSTM	Autoenkoder	Konwolucyjna Sieć Neuronowa (Klasyfikacja)	Random Forest	Transformer (NLP)
Dziedzina	Uczenie głębokie	Uczenie głębokie	Uczenie głębokie	Uczenie maszynowe	Uczenie głębokie
Rodzina	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Rok powstania	1997	2006	1998	2001	2017
Twórca	Hochreiter, S. & Schmidhuber, J.	Hinton, G.E. & Salakhutdinov, R.R.	LeCun, Y. et al.	Breiman, L.	Vaswani, A. et al.
Typ	Recurrent neural network (gated memory cell)	Neural network (encoder-decoder)	Deep neural network (convolutional)	Ensemble (bagging of decision trees)	Attention-based deep neural network
Źródło pierwotne	Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998). Gradient-Based Learning Applied to Document Recognition. Proceedings of the IEEE, 86(11), 2278–2324.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
Inne nazwy	LSTM (Uzun Kısa Dönem Bellek Ağı), long short-term memory, LSTM network, recurrent neural network with memory cells	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Transformer Modeli (NLP), attention-based language model, self-attention network, transformer NLP
Pokrewne	5	4	5	4	4
Podsumowanie	LSTM (Long Short-Term Memory) is a recurrent neural network architecture, introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997, that can learn long-term dependencies in sequential data and is widely used for time-series and sequence prediction. It keeps an internal memory that lets information persist across many time steps.	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.