Abstract
Deep learning prospered as a distinct era of research and fragment of a wider family of machine learning, based on a set of algorithms that strengthen to model high-level abstractions in data. It tries to imitate the human intellect and learns from complicated input data and resolve different types of difficult and complex tasks. Because of Deep Learning, it was successful to deal with different input data types such as text, sound, and images in various fields. Improvement in deep-learning research has already influenced the search for speech recognition, automatic navigation systems, parallel computations, image processing, ImageNet, natural language processing, representation learning, Google translate, etc. Here, we present a review of DL and its applications including the recent development in natural language processing (NLP).
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References
Arel, I., Rose, D.C., Karnowski, T.P.: Deep machine learning—A new frontier in artificial intelligence research [research frontier]. Comput. Intell. Mag.-IEEE 5(4), 13–18 (2010)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. Elsevier 61, 85–117 (2015)
Coates, A., Lee, H., Ng, A.Y.: An analysis of single layer networks in unsupervised feature learning. AISTATS 15, 215–223 (2011)
Bengiom Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Bengio, Y., Courville, A.: Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)
Hinton, G.E., Osindero, S.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 2006
Bengio, Y., Lamblin, P.: Greedy layer wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153 (2007)
R. Salakhutdinov, G. E. Hinton, “Deep Boltzmann machines, “International Conference on Artificial Intelligence and Statistics, 2009
Hinton, G.E.: Learning distributed representations of concepts. In:: Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA (1986)
Bengio, Y., Ducharme, R.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)
Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)
Collobert, R., Weston, J.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 24932537 (2011)
Qin, P., Xu, W., Guo, J.: An empirical convolutional neural network approach for semantic relation classification. Neuro Comput. 190, 1–9 (2016)
Mohamed, A.: Deep belief networks for phone recognition. In: Nips Workshop on Deep Learning for Speech Recognition And Related Applications, vol. 1, pp. 635–645 (2009)
Deng, L., Yu, D.: Deep learning: methods and applications. In: Foundations and Trends® in Signal Processing, vol. 7, pp. 197–387 (2014)
Yann, L., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Edgbaston: Formative Assessment: A Key to Deep Learning, Birmingham, UK (2007)
Sree, P.K., Babu, I.R., Devi, N.U.: A fast multiple attractor cellular automata with modified clonal classifier. promoter region prediction. J. Bioinf. Intell. Control 3, 1–6 (2014). https://doi.org/10.1166/jbic.2014.1077
Karhunen, J., Tapani, R., Cho, K.H.”: Unsupervised deep learning: a short review. In: Advances in Independent Component Analysis and Learning Machines, vol. 125 (2015)
Alison Rushton, A.: University of Birmingham
Sree, P.K., Babu, I.R, Devi, N.U.: Investigating an artificial immune system to strengthen the protein structure prediction and protein coding region identification using cellular automata classifier. Int. J. Bioinf. Res. Appl., 5(6), 647–662 (2009) (Inderscience Journals, UK)
Sree, P.K., Babu, I.R., Devi, N.U.: Identification of promoter region in genomic DNA using cellular automata based text clustering. Int. Arab. J. Inf. Technol. (IAJIT) 7(1), 75–78 (2010). ISSN: 1683-3198H Index (Citation Index): 05 (SC Imago, www.scimagojr.com)(Eleven Years Old Journal)(SCI Indexed Journal)
Sree, P.K., Babu, I.R., Devi, N.U.: Fast multiple attractor cellular automata with modified clonal classifier for coding region prediction in human genome. J. Bioinform. Intell. Control 3, 16 (2014). https://doi.org/10.1166/jbic.2014.107 (American Scientific Publications, USA)
Stefan, G.: Detecting unexpected obstacles for self-driving cars: fusing deep learning and geometric modeling
Mohammed, A., Barjasteh, I., Al-Qassab, H., Radha, H.: Fellow, IEEE “Deep Learning Algorithm for Autonomous Driving using GoogleNet”
Soniya: A review on advances in deep learning. In: IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI) (2015)
Geist, A., Beguelin, A., Dongarra, J., Jiang, W., Manchek, R., Sunderam, V.: PVM: Parallel Virtual Machine—A Users’ Guide and Tutorial for Networked Parallel Computing. MIT Press (1994)
Quinn, M.J.: Parallel Programming in C with MPI and Open MP. Tata McGraw-Hill Higher Education (2003)
Krizhevsky, A.: One weird trick for parallelizing convolutional neural networks. Comput. Res. Repository (CoRR), abs/1404.5997 (2014)
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q.V., Mao, M.Z., Ranzato, M., Senior, A.W., Tucker, P.A., Yang, K., Ng, A.Y.: Large scale distributed deep networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceeding of a meeting held December 3–6, 2012, pp. 1232–1240, Lake Tahoe, Nevada, United States (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image Net classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held on December 3
Yang, W.: Recognizing human actions from still images with latent poses. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)
Lee, H.: Can Deep Neural Networks Match the Related Objects?: A Survey on Image Net-trained Classification Models (2017)
Larochelle, H., Bengio, Y., Louradour, J., Lamblin, P.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1–40 (2009)
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning. J. Mach. Learn. Res. 11, 625– 660 (2011)
Araque, O., Corcuera-Platas, I., Sánchez-Rada, J., Iglesias, A.: Enhancing deep learning sentiment analysis with ensemble techniques in social applications. Expert Syst. Appl. 77, 236246 (2017)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. ICLR Workshop (2013)
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1555–1565 (2014)
Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959–962 (2015)
Lauren, P., Qu, G., Zhang, F., Lendasse, A.: Discriminant document embedding’s with an extreme learning machine for classifying clinical narratives. Neurocomputing 1–10 (2017)
Fu, X., Liu, W., Xu, Y., Cui, L.: Combine How Net lexicon to train phrase recursive auto encoder for sentence-level sentiment analysis. Neuro comput. 241, 18–27 (2017)
Ren, Y., Wang, R., Ji, D.: A topic-enhanced word embedding for Twitter sentiment classification. Inf. Sci. 369, 188–198 (2016)
Giatsoglou, M., Vozalis, M., Diamantaras, K., Vakali, A., Sarigiannidis, G., Chatzisavvas, K.: Sentiment analysis leveraging emotions and word embedding’s. Expert Syst. Appl. 69, 214–224 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1746–1751 (2014)
Du, T., Shanker, V.: Deep Learning for Natural Language Processing. Eecis. Udel. Edu, pp. 1–7 (2009)
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Sultana, J., Usha Rani, M., Farquad, M.A.H. (2020). An Extensive Survey on Some Deep-Learning Applications. In: Venkata Krishna, P., Obaidat, M. (eds) Emerging Research in Data Engineering Systems and Computer Communications. Advances in Intelligent Systems and Computing, vol 1054. Springer, Singapore. https://doi.org/10.1007/978-981-15-0135-7_47
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DOI: https://doi.org/10.1007/978-981-15-0135-7_47
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