Branch of machine learning
Representing images on multiple layers of abstraction in deep learning[1]
Deep learning is the subset of machine learning methods based on artificial neural networks (ANNs) with representation learning . The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised , semi-supervised or unsupervised .[2]
Deep-learning architectures such as deep neural networks , deep belief networks , recurrent neural networks , convolutional neural networks and transformers have been applied to fields including computer vision , speech recognition , natural language processing , machine translation , bioinformatics , drug design , medical image analysis , climate science , material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.[3] [4] [5]
Artificial neural networks were inspired by information processing and distributed communication nodes in biological systems . ANNs have various differences from biological brains. Specifically, artificial neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.[6] [7] ANNs are generally seen as low quality models for brain function.[8]
^ Schulz, Hannes; Behnke, Sven (1 November 2012). "Deep Learning" . KI - Künstliche Intelligenz . 26 (4): 357–363. doi :10.1007/s13218-012-0198-z . ISSN 1610-1987 . S2CID 220523562 .
^ LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep Learning" (PDF) . Nature . 521 (7553): 436–444. Bibcode :2015Natur.521..436L . doi :10.1038/nature14539 . PMID 26017442 . S2CID 3074096 .
^ Ciresan, D.; Meier, U.; Schmidhuber, J. (2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition . pp. 3642–3649. arXiv :1202.2745 . doi :10.1109/cvpr.2012.6248110 . ISBN 978-1-4673-1228-8 . S2CID 2161592 .
^ Krizhevsky, Alex; Sutskever, Ilya; Hinton, Geoffrey (2012). "ImageNet Classification with Deep Convolutional Neural Networks" (PDF) . NIPS 2012: Neural Information Processing Systems, Lake Tahoe, Nevada . Archived (PDF) from the original on 2017-01-10. Retrieved 2017-05-24 .
^ "Google's AlphaGo AI wins three-match series against the world's best Go player" . TechCrunch . 25 May 2017. Archived from the original on 17 June 2018. Retrieved 17 June 2018 .
^ Marblestone, Adam H.; Wayne, Greg; Kording, Konrad P. (2016). "Toward an Integration of Deep Learning and Neuroscience" . Frontiers in Computational Neuroscience . 10 : 94. arXiv :1606.03813 . Bibcode :2016arXiv160603813M . doi :10.3389/fncom.2016.00094 . PMC 5021692 . PMID 27683554 . S2CID 1994856 .
^ Bengio, Yoshua; Lee, Dong-Hyun; Bornschein, Jorg; Mesnard, Thomas; Lin, Zhouhan (13 February 2015). "Towards Biologically Plausible Deep Learning". arXiv :1502.04156 [cs.LG ].
^ "Study urges caution when comparing neural networks to the brain" . MIT News | Massachusetts Institute of Technology . 2022-11-02. Retrieved 2023-12-06 .