Skip to main content
SpringerLink
Log in

Deep Learning Methods and Applications

  • Chapter
  • First Online:
Deep Learning: Convergence to Big Data Analytics

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

This chapter introduces the various methods existing beneath the umbrella of deep learning paradigm, their intricate details, and their applications in various fields. Deep learning has substantially improved the predictive capacity of computing devices, due to the availability of big data, with the help of superior learning algorithms. It has made it possible as well as practical to integrate machine learning with sophisticated applications including image recognition, object detection, self-driving cars, drug discovery, and disease detection. The superior and reliable performance of deep learning methods has attracted the attention of researchers working in every field of science to utilize their strengths in order to solve problems. In addition to that, the knowledge reuse in deep learning is an interesting aspect of this technology which will also be discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 16.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

CNN:

Convolutional neural network

DNN:

Deep neural networks

RNN:

Recurrent neural network

LSTM:

Long short-term memory

IR:

Information retrieval

BoW:

Bag-of-words

CBIR:

Content-based image retrieval

NLP:

Natural language processing

ML:

Machine learning

MTL:

Multitask learning

References

  • Ahmad J, Muhammad K, Baik SW (2017a) Medical image retrieval with compact binary codes generated in frequency domain using highly reactive convolutional features. J Med Syst 42:24. https://doi.org/10.1007/s10916-017-0875-4

    Article  Google Scholar 

  • Ahmad J, Sajjad M, Mehmood I, Baik SW (2017b) SiNC: saliency-injected neural codes for representation and efficient retrieval of medical radiographs. PloS One 12:e0181707

    Article  Google Scholar 

  • Ahmad J, Muhammad K, Bakshi S, Baik SW (2018a) Object-oriented convolutional features for fine-grained image retrieval in large surveillance datasets. Future Gener Comput Syst 81:314–330. https://doi.org/10.1016/j.future.2017.11.002

    Article  Google Scholar 

  • Ahmad J, Muhammad K, Lloret J, Baik SW (2018b) Efficient conversion of deep features to compact binary codes using fourier decomposition for multimedia Big Data. IEEE Trans Ind Inf

    Google Scholar 

  • Badshah AM, Ahmad J, Rahim N, Baik SW (2017) Speech emotion recognition from spectrograms with deep convolutional neural network. In: 2017 international conference on platform technology and service (PlatCon), 2017. IEEE, pp 1–5

    Google Scholar 

  • Caruana R (1997) Multitask learning. Mach Learn 28:41–75

    Article  Google Scholar 

  • Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, 2008. ACM, pp 160–167

    Google Scholar 

  • Deng L, Hinton G, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP), 2013. IEEE, pp 8599–8603

    Google Scholar 

  • Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38:295–307

    Article  Google Scholar 

  • Duong L, Cohn T, Bird S, Cook P (2015) Low resource dependency parsing: cross-lingual parameter sharing in a neural network parser. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing, vol 2 (Short Papers), 2015, pp 845–850

    Google Scholar 

  • Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542:115

    Article  Google Scholar 

  • Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, 2015, pp 1440–1448

    Google Scholar 

  • Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning (ICML-11), 2011, pp 513–520

    Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp 770–778

    Google Scholar 

  • Hinton G et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82–97

    Article  Google Scholar 

  • Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507

    Article  MathSciNet  Google Scholar 

  • Jain A, Tompson J, LeCun Y, Bregler C (2014) Modeep: a deep learning framework using motion features for human pose estimation. In: Asian conference on computer vision, 2014. Springer, pp 302–315

    Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Paper presented at the proceedings of the 25th international conference on neural information processing systems, vol 1, Lake Tahoe, Nevada

    Google Scholar 

  • LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1:541–551

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  Google Scholar 

  • Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110. https://doi.org/10.1023/B:VISI.0000029664.99615.94

    Article  Google Scholar 

  • Lu N, Li T, Ren X, Miao H (2017) A deep learning scheme for motor imagery classification based on restricted boltzmann machines. IEEE Trans Neural Syst Rehabilitation Eng 25:566–576

    Article  Google Scholar 

  • Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vision 42:145–175

    Article  Google Scholar 

  • Rajpurkar P et al (2017) Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:171105225

    Google Scholar 

  • Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V (2015) Massively multitask networks for drug discovery. arXiv preprint arXiv:150202072

    Google Scholar 

  • Ullah A, Ahmad J, Muhammad K, Sajjad M, Baik SW (2018) Action recognition in video sequences using deep Bi-directional LSTM with CNN features IEEE. Access 6:1155–1166

    Article  Google Scholar 

  • Xu K et al (2015) Show, attend and tell: neural image caption generation with visual attention. In: International conference on machine learning, 2015, pp 2048–2057

    Google Scholar 

  • Yang W, Lu Z, Yu M, Huang M, Feng Q, Chen W (2012) Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single-and multiphase contrast-enhanced CT images. J Digital Imaging 25:708–719. https://doi.org/10.1007/s10278-012-9495-1

    Article  Google Scholar 

  • Yang Y, Hospedales TM (2016) Trace norm regularised deep multi-task learning. arXiv preprint arXiv:160604038

    Google Scholar 

  • Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. arXiv preprint arXiv:14092329

    Google Scholar 

  • Zhang R, Isola P, Efros AA (2016) Colorful image colorization. In: European conference on computer vision, 2016. Springer, pp 649–666

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Islamia College Peshawar, Peshawar, Pakistan

    Jamil Ahmad, Haleem Farman & Zahoor Jan

Authors
  1. Jamil Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haleem Farman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zahoor Jan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jamil Ahmad .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ahmad, J., Farman, H., Jan, Z. (2019). Deep Learning Methods and Applications. In: Deep Learning: Convergence to Big Data Analytics. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-3459-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3459-7_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3458-0

  • Online ISBN: 978-981-13-3459-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation