Definitions
Deep Learning is a family of machine learning methods based on the composition of multiple (simple) processing layers, in order to learn complex (nonlinear) functions, which can be used to cope with many challenging application scenarios (e.g., Computer Vision, Natural Language Processing, Speech Recognition and Genomics (Le Cun et al. 2015)).
Overview
In the last few years, advances in digital sensors, computation, communications, and storage technologies and the large diffusion of IoT devices facilitated the production of huge collections of heterogeneous data, which are also susceptible to rapid changes over time. The term Big Data is used to define this kind of phenomenon (Wu et al. 2014).
Deep learning (DL) includes a wide range of machine learning techniques that aims at training artificial neural networks (ANNs) composed of a large number of hidden layers. These methods have been effectively used to tackle different types of problems (e.g., Computer Vision, Natural...
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Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127
Bengio Y (2012) Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML workshop on unsupervised and transfer learning, pp 17–36
Byeon W, Breuel TM, Raue F, Liwicki M (2015) Scene labeling with LSTM recurrent neural networks. In: CVPR, IEEE Computer Society, pp 3547–3555
Chandar APS, Lauly S, Larochelle H, Khapra MM, Ravindran B, Raykar V, Saha A (2014) An autoencoder approach to learning bilingual word representations. In: Proceedings of the 27th international conference on neural information processing systems – vol 2, NIPS’14. MIT Press, Cambridge, MA, pp 1853–1861, http://dl.acm.org/citation.cfm?id=2969033.2969034
Dahl G, Ranzato M, rahman Mohamed A, Hinton GE (2010) Phone recognition with the mean-covariance restricted Boltzmann machine. In: Lafferty JD, Williams CKI, Shawe-Taylor J, Zemel RS, Culotta A (eds) Advances in neural information processing systems 23. Curran Associates, Inc., pp 469–477
Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn 58:121–134
Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the twenty-eight international conference on machine learning, ICML
Graves A, Fernández S, Liwicki M, Bunke H, Schmidhuber J (2007) Unconstrained online handwriting recognition with recurrent neural networks. In: Proceedings of the 20th international conference on neural information processing systems, NIPS’07. Curran Associates Inc., pp 577–584
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647, http://www.sciencemag.org/cgi/content/abstract/313/5786/504
Huang PS, He X, Gao J, Deng L, Acero A, Heck L (2013) Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM, pp 2333–2338
Jia S, Lansdall-Welfare T, Cristianini N (2016) Gender classification by deep learning on millions of weakly labelled images. In: 2016 IEEE 16th international conference on data mining workshops (ICDMW). IEEE, pp 462–467
Kussul N, Lavreniuk M, Skakun S, Shelestov A (2017) Deep learning classification of land cover and crop types using remote sensing data. IEEE Geosci Remote Sens Lett 14(5):778–782
Le Cun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Lebret R, Collobert R (2015) “The sum of its parts”: joint learning of word and phrase representations with autoencoders. CoRR abs/1506.05703
Lv Y, Duan Y, Kang W, Li Z, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
Ma X, Yu H, Wang Y, Wang Y (2015) Large-scale transportation network congestion evolution prediction using deep learning theory. https://doi.org/10.1371/journal.pone.0119044
Najafabadi MM, Villanustre F, Khoshgoftaar TM, Seliya N, Wald R, Muharemagic E (2015) Deep learning applications and challenges in big data analytics. J Big Data 2(1):1
Paula EL, Ladeira M, Carvalho RN, Marzagão T (2016) Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 954–960
Ren H, Chai Y, Qu J, Ye X, Tang Q (2017) A novel adaptive fault detection methodology for complex system using deep belief networks and multiple models: a case study on cryogenic propellant loading system. Neurocomputing 275:2111–2125
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on machine learning, ICML’07. ACM, New York, pp 791–798. https://doi.org/10.1145/1273496.1273596, http://doi.acm.org/10.1145/1273496.1273596
Shen Y, He X, Gao J, Deng L, Mesnil G (2014) Learning semantic representations using convolutional neural networks for web search. In: Proceedings of the 23rd international conference on world wide web. ACM, pp 373–374
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th international conference on neural information processing systems – vol 2, NIPS’14. MIT Press, Cambridge, MA, pp 3104–3112. http://dl.acm.org/citation.cfm?id=2969033.2969173
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Computer vision and pattern recognition (CVPR)
Torrey L, Shavlik J (2009) Transfer learning. In: Soria E, Martin J, Magdalena R, Martinez M, Serrano A (eds) Handbook of research on machine learning applications and trends: algorithms, methods, and techniques, vol 1. IGI Global, Hershey, p 242
Vincent P, Larochelle H, Bengio Y, Manzagol P (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, ICML’08, pp 1096–1103
Wu Y, Wang Z, Ji Q (2013) Facial feature tracking under varying facial expressions and face poses based on restricted Boltzmann machines. In: 2013 IEEE conference on computer vision and pattern recognition, Portland, 23–28 June 2013, pp 3452–3459. https://doi.org/10.1109/CVPR.2013.443
Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107
Zhang S, Yang M, Wang X, Lin Y, Tian Q (2013) Semantic-aware co-indexing for image retrieval. In: Proceedings of the IEEE international conference on computer vision, pp 1673–1680
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Folino, G., Guarascio, M., Haeri, M.A. (2019). Deep Learning on Big Data. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_307
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DOI: https://doi.org/10.1007/978-3-319-77525-8_307
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