Uporedite metode

	Konvoluciona neuronska mreža (klasifikacija)	Autoenkoder	Slučajna šuma
Oblast	Duboko učenje	Duboko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning
Godina nastanka	1998	2006	2001
Tvorac	LeCun, Y. et al.	Hinton, G.E. & Salakhutdinov, R.R.	Breiman, L.
Tip	Deep neural network (convolutional)	Neural network (encoder-decoder)	Ensemble (bagging of decision trees)
Temeljni izvor	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Drugi nazivi	CNN (Evrişimli Sinir Ağı — Sınıflandırma), CNN classification, ConvNet, convolutional network classifier	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Srodne	5	4	4
Sažetak	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.	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.