Top

Published in:

2021 | OriginalPaper | Chapter

Deep Learning and its Applications: A Real-World Perspective

Authors : Lakshmi Haritha Medida, Kasarapu Ramani

Published in: Deep Learning and Edge Computing Solutions for High Performance Computing

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Deep Learning (DL), a division of Machine Learning (ML) is a highly focused field of data science. DL is the most active approach for ML. DL algorithms excerpt the complex high-level data features via a hierarchical learning process. These complex abstractions at a given hierarchical level are learned based on the abstractions framed in the previous level of the hierarchy. The capability of DL to analyze and learn huge quantities of unsupervised data makes it a powerful tool for Big Data Analytics where data are mostly unorganized and unlabeled. With the significant advancements and tremendous performance of DL, it is broadly used across numerous domains such as business, health, government, and so on. This chapter focuses on the overview and applications of DL from a real-world perspective, which covers a variety of areas such as Speech Recognition, Text Classification, Document Summarization, Fraud Detection, Visual Recognition, Personalization’s, and so on.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Understanding Deep Learning: Case Study Based Approach

next chapter Applying Blockchain in Agriculture: A Study on Blockchain Technology, Benefits, and Challenges

https://devblogs.nvidia.com/deep-learning-nutshell-history-training/

S.J. Arora, R. Singh, Automatic speech recognition: A review. Int. J. Comput. Appl. 60(9), 34–44 (2012). https://doi.org/10.5120/9722-4190CrossRef

V. Passricha and R.K. Aggarwal (2018). Convolutional Neural Networks for Raw Speech Recognition, From Natural to Artificial Intelligence—Algorithms and Applications, Ricardo Lopez-Ruiz, IntechOpen, DOI: https://doi.org/10.5772/intechopen.80026. https://www.intechopen.com/books/from-natural-to-artificial-intelligence-algorithms-and-applications/convolutional-neural-networks-for-raw-speech-recognition

D. Mayank, R.K. Aggarwal, Implementing a speech recognition system interface for Indian languages, proc.of the JCNLP-08 workshop on NLP, Hyderabad, India (2008), Pp. 105–112

A.B. Nassif, I. Shahin, I. Attili, M. Azzeh, K. Shaalan, Speech recognition using deep neural networks: A systematic review. IEEE Access 7, 19143–19165 (2019). https://doi.org/10.1109/ACCESS.2019.2896880CrossRef

A. Raju, D. Filimonov, G. Tiwari, G. Lan, and A. Rastrow, “Scalable Multi Corpora Neural Language Models for ASR,” in Proc. Interspeech (2019), pp. 3910–3914. arXiv:1907.01677

S. Schneider, A. Baevski, R. Collobert, and M. Auli. wav2vec: Unsupervised pre-training for speech recognition. CoRR, abs/1904.05862 (2019). arXiv:1904.05862v4

D.S. Park, W. Chan, Y. Zhang, C.-C. Chiu, B. Zoph E.D. Cubuk, and Q.V. Le. Specaugment: A simple data augmentation method for automatic speech recognition (2019). arXiv:1904.08779v2

https://monkeylearn.com/text-classification/

10.

Example of sentiment analysis, Digital Image, Monkey Learn, Jan 2 (2020). https://monkeylearn.com/sentiment-analysis/

11.

Example of topic modelling, Digital Image, Analytics Vidhya, April 23 (2018), https://www.analyticsvidhya.com/blog/2018/04/a-comprehensive-guide-to-understand-and-implement-text-classification-in-python/

12.

Example of intent detection, Digital Image, Towards Data science, Jan 7 (2018). https://towardsdatascience.com/sentiment-analysis-concept-analysis-and-applications-6c94d6f58c17

13.

B. Guo, C. Zhang, J. Liu, X. Ma, Improving text classification with weighted word embeddings via a multi-channel TextCNN model. Neurocomputing 363, 366–374 (2019). https://doi.org/10.1016/j.neucom.2019.07.052CrossRef

14.

Z. Lan, M. Chen, S. Goodman, K. Gimpel, P. Sharma, and R. Soricut, “ALBERT: A Lite BERT for Selfsupervised Learning of Language Representations”, In International Conference on Learning Representations, 2020.arXiv:1909.11942v6

15.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. “BERT: Pre-training of deep bidirectional transformers for language understanding”. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186, Minneapolis, Minnesota, June 2019. Association for Computational Linguistics. doi: https://doi.org/10.18653/v1/N19-1423

16.

L. Huang, D. Ma, S. Li, X. Zhang, and H. Wang, Text level graph neural network for text classification (2019). arXiv:1910.02356

17.

M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E.D. Trippe, J.B. Gutierrez and K. Kochut, Text Summarization Techniques: A Brief Survey, (2017). arXiv:1707.02268

18.

K. Chauhan, “Unsupervised Text Summarization using Sentence Embedding” (2018). https://medium.com/jatana/unsupervised-text-summarization-using-sentence-embeddings-adb15ce83db1

19.

“Bootstrap Your Text Summarization Solution with the Latest Release from NLP-Recipes”, https://techcommunity.microsoft.com/t5/ai-customer-engineering-team/bootstrap-your-text-summarization-solution-with-the-latest/ba-p/1268809#

20.

Y. Liu,“Fine-tune bert for extractive summarization”, (2019). arXiv preprint arXiv:1903.10318

21.

X. Zhang, F. Wei and M. Zhou, “HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization”, In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019, Florence, Italy, July 2019, Pp 5059–5069. Association for Computational Linguistics

22.

H. Wang, X. Wang, W. Xiong, M. Yu, X. Guo, S. Chang and W.Y. Wang, “Self-supervised learning for contextualized extractive summarization”, 2019.arXiv:1906.04466v1

23.

Q. Zhou, N. Yang, F. Wei, S. Huang, M. Zhou and T. Zhao, “Neural document summarization by jointly learning to score and select sentences”, In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15–20, 2018, Volume 1: Long Papers, Pp 654–663. Association for Computational Linguistics

24.

J. Xu, and G. Durrett, “Neural extractive text summarization with syntactic compression”, In EMNLP2019.arXiv:1902.00863v2

25.

Z. Zhang et al, A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection, Security and Communication Networks, Special Issue: Machine Learning for Wireless Multimedia Data Security, Vol. 2018, 9 pages. doi:https://doi.org/10.1155/2018/5680264

26.

A.M. Mubalaike and E. Adali, "Deep Learning Approach for Intelligent Financial Fraud Detection System," 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, (2018), pp. 598–603

27.

S. Sorournejad et al, “A Survey of Credit Card Fraud Detection Techniques: Data and Technique Oriented Perspective”, Nov 2016.arXiv:1611.06439

28.

L. Zheng, C. Chen, Y. Liu, B. Wu, X. Wu, L. Wang, L. Wang, J. Zhou and S. Yang, “Industrial scale privacy preserving deep neural network” (2020). arXiv:2003.05198

29.

D. Wang, J. Lin, P. Cui, Q. Jia, Z. Wang, Y. Fang, Q. Yu, J. Zhou, S. Yang and Y. Qi, “A Semi-supervised Graph Attentive Network for Financial Fraud Detection” (2020). arxiv:2003.01171v1

30.

R.E. Yancey, “Multi-stream Faster RCNN for Mitosis Counting in Breast Cancer Images” (2020). arxiv:2002.03781v1

31.

D. Kanurakaran, “Simple Image classification using deep learning - deep learning series 2”, May 2018. https://medium.com/intro-to-artificial-intelligence/simple-image-classification-using-deep-learning-deep-learning-series-2-5e5b89e97926

32.

M.Z. Alom et al., A state-of-the-art survey on deep learning theory and architectures. Electronics 8(292), 1–67 (2019). https://doi.org/10.3390/electronics8030292CrossRef

33.

J. Schneider & M. Vlachos. Mass personalization of deep learning (2019). arXiv preprint arXiv:1909.02803

34.

X. Zhou, J. Zhuo and P. Krähenbühl, “Bottom-up Object Detection by Grouping Extreme and Center Points”, In CVPR (2019). arXiv:1901.08043v3

35.

Y. Chen, M. Rohrbach, Z. Yan, S. Yan, J. Feng, and Y. Kalantidis, “Graph based global reasoning networks”, In CVPR (2019b)

36.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition” In CVPR (2016)

37.

K. He, X. Zhang, S. Ren, and J. Sun,“Identity mappings in deep residual networks”, In ECCV, (2016)

38.

S. Xie, C. Sun, J. Huang, Z. Tu, and K. Murphy, Rethinking spatiotemporal feature learning for video understanding (2017). arXiv:1712.04851

39.

J. Hu, L. Shen, and G. Sun, Squeeze-and-excitation networks, In CVPR (2018)

40.

Y. Chen, J. Li, H. Xiao, X. Jin, S. Yan, and J. Feng, Dual path networks, In NeurIPS (2017)

41.

Q. Zhao, T. Sheng, Y. Wang, Z. Tang, Y. Chen, L. Cai, & H. Ling, M2Det: A single shot object detector based on multilevel feature pyramid network, In AAAI (2019)

42.

N. Justesen & P. Bontrager & J. Togelius and S. Risi, Deep Learning for Video Game Playing, IEEE Transactions on Games. https://doi.org/10.1109/TG.2019.2896986

43.

M. Naumov, D. Mudigere, et al., Deep learning recommendation model for personalization and recommendation systems (2019). arXiv:1906.00091

44.

S. Rendle, Factorization machines, In Proc. 2010 IEEE International Conference on Data Mining, Pp 995–1000, (2010)

45.

R. Wang, B. Fu, G. Fu, and M. Wang, Deep & cross network for ad click predictions, In Proc. ADKDD, page 12 (2017)

46.

C. Wang, Z. Guo, J. Li, P. Pan and G. Li, A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation, 2020. arxiv:2004.06651v1

47.

R. Agrawal, J. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, in Proceedings ACM SIGMOD International Conference on Management of Data (SIGMOD), ed. by L. M. Haas, A. Tiwary, (1998), pp. 94–105

48.

M. Ostendorff, T. Ruas, M. Schubotz, G. Rehm and B. Gipp, “Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles”, 2020. arxiv:2003.09881v1

Title: Deep Learning and its Applications: A Real-World Perspective
Authors: Lakshmi Haritha Medida
Kasarapu Ramani
Publisher: Springer International Publishing
Book: Deep Learning and Edge Computing Solutions for High Performance Computing
Print ISBN: 978-3-030-60264-2

Electronic ISBN: 978-3-030-60265-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-60265-9_10

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"