পদ্ধতির তুলনা করুন

	অটোএনকোডার	Convolutional Neural Network	Random Forest	ট্রান্সফরমার (এনএলপি)
ক্ষেত্র	গভীর শিখন	গভীর শিখন	যন্ত্র শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2006	1998	2001	2017
প্রবর্তক	Hinton, G.E. & Salakhutdinov, R.R.	LeCun, Y. et al.	Breiman, L.	Vaswani, A. et al.
ধরন	Neural network (encoder-decoder)	Deep neural network (convolutional)	Ensemble (bagging of decision trees)	Attention-based deep neural network
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	4	5	4	4
সারসংক্ষেপ	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.