Skip to main content
SpringerLink
Log in
Book cover

Advanced Deep Learning for Engineers and Scientists pp 23–60Cite as

Deep Learning Applications with Python

Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

The applications of deep learning extend to many aspects of daily life and are not confined to the domains of computer science alone. From face recognition to the smart grid domain, deep learning has proved itself to be an effective tool in producing state-of-the-art results. This chapter discusses how the usage of a high-level language like Python and its compatibility with deep learning frameworks and its collection of utility libraries facilitates practitioners in the development process. The chapter further dwells into details of the implementation of deep learning techniques applied to different applications, namely, facial recognition, fingerprint recognition, character recognition, smart grids, and renewable energy, by providing a brief history of how the technology has influenced the domain and details regarding the common datasets used as well as a code implementation in Python thoroughly covering the different steps.

Keywords

  • Deep learning
  • Convolutional neural networks
  • Computer vision
  • Face recognition
  • Fingerprint recognition
  • Character recognition
  • Smart grids
  • Renewable energy

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   119.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Acharya, S., Pant, A.K., Gyawali, P.K.: Deep learning based large scale handwritten Devanagari character recognition. In 2015 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 1-6). IEEE (2015, December)

    Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    CrossRef  Google Scholar 

  3. Aslam, M., Lee, J.M., Kim, H.S., Lee, S.J., Hong, S.: Deep learning models for long-term solar radiation forecasting considering microgrid installation: a comparative study. Energies. 13(1), 147 (2020)

    CrossRef  Google Scholar 

  4. California ISO, California ISO – Renewables and emissions reports, caiso.com, http://www.caiso.com/market/Pages/ReportsBulletins/RenewablesReporting.aspx (accessed May 17, 2020)

  5. Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A.: Vggface2: a dataset for recognising faces across pose and age. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (pp. 67-74). IEEE (2018, May)

    Google Scholar 

  6. Centeno, Iván de Paz. 2020. “Mtcnn”. Pypi. https://pypi.org/project/mtcnn/

  7. Chen, L., Wang, S., Fan, W., Sun, J., Naoi, S.: Beyond human recognition: a CNN-based framework for handwritten character recognition. In 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) (pp. 695–699). IEEE (2015, November)

    Google Scholar 

  8. Cohen, G., Afshar, S., Tapson, J., Van Schaik, A.: EMNIST: extending MNIST to handwritten letters. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 2921–2926). IEEE (2017, May).

    Google Scholar 

  9. Darlow, L.N., Rosman, B.: Fingerprint minutiae extraction using deep learning. In 2017 IEEE International Joint Conference on Biometrics (IJCB) (pp. 22–30). IEEE (2017, October)

    Google Scholar 

  10. De Campos, T., Bodla, R. B., Varma, M.: The chars74k dataset (2009)

    Google Scholar 

  11. FVC-onGoing 2009 On-line evaluation of fingerprint recognition algorithms, https://biolab.csr.unibo.it/fvcongoing

  12. Garcia-Salicetti, S., Beumier, C., Chollet, G., Dorizzi, B., Les Jardins, J.L., Lunter, J., Ni, Y., Petrovska-Delacrétaz, D.: BIOMET: a multimodal person authentication database including face, voice, fingerprint, hand and signature modalities. In: International conference on audio- and video-based biometric person authentication, pp. 845–853. Springer, Berlin, Heidelberg (2003, June)

    CrossRef  Google Scholar 

  13. Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting—an approach using AutoEncoder and LSTM neural networks. In 2016 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 002858–002865). IEEE (2016, October)

    Google Scholar 

  14. Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J.: Ms-celeb-1m: a dataset and benchmark for large-scale face recognition. In: European conference on computer vision, pp. 87–102. Springer, Cham (2016, October)

    Google Scholar 

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

    Google Scholar 

  16. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database forstudying face recognition in unconstrained environments (2008, October)

    Google Scholar 

  17. Inman, R.H., Pedro, H.T., Coimbra, C.F.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39(6), 535–576 (2013)

    CrossRef  Google Scholar 

  18. Jain, A.K., Li, S.Z.: Handbook of face recognition, vol. 1. Springer, New York (2011)

    MATH  Google Scholar 

  19. Jiang, B., Ren, Q., Dai, F., Xiong, J., Yang, J., Gui, G.: Multi-task cascaded convolutional neural networks for real-time dynamic face recognition method. In: International conference in communications, signal processing, and systems, pp. 59–66. Springer, Singapore (2018, July)

    Google Scholar 

  20. Kaggle, “California Renewable Production 2010–2018”, kaggle.com, https://www.kaggle.com/cheedcheed/california-renewable-production-20102018 (accessed May 17, 2020)

  21. Kaggle, “Smart meters in London”, kaggle.com, https://www.kaggle.com/jeanmidev/smart-meters-in-london/ (accessed May 17, 2020)

  22. Kaggle, “International Energy Statistics”, kaggle.com, https://www.kaggle.com/unitednations/international-energy-statistics (accessed May 17, 2020)

  23. Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The megaface benchmark: 1 million faces for recognition at scale. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4873–4882) (2016)

    Google Scholar 

  24. Koch, G., Zemel, R., Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In ICML deep learning workshop (Vol. 2) (2015, July)

    Google Scholar 

  25. Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on data mining applications in sustainability (SIGKDD), vol. 25, pp. 59–62. Citeseer, San Diego, CA (2011, August)

    Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105) (2012)

    Google Scholar 

  27. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE. 86(11), 2278–2324 (1998)

    CrossRef  Google Scholar 

  28. Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)

    CrossRef  Google Scholar 

  29. Malli, R., 2020. Keras-Vggface. [online] PyPI. Available at: https://pypi.org/project/keras-vggface [Accessed 17 May 2020]

  30. Marino, D.L., Amarasinghe, K. and Manic, M., 2016, October. Building energy load forecasting using deep neural networks. In IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society (pp. 7046–7051). IEEE

    Google Scholar 

  31. Mbuwir, B., Ruelens, F., Spiessens, F., Deconinck, G.: Reinforcement learning-based battery energy management in a solar microgrid. Energy-Open. 2(4), 36 (2017)

    Google Scholar 

  32. Ortega-Garcia, J., Fierrez-Aguilar, J., Simon, D., Gonzalez, J., Faundez-Zanuy, M., Espinosa, V., Satue, A., Hernaez, I., Igarza, J.J., Vivaracho, C., Escudero, D.: MCYT baseline corpus: a bimodal biometric database. IEE Proceedings-Vision, Image and Signal Processing. 150(6), 395–401 (2003)

    CrossRef  Google Scholar 

  33. Parkhi, O.M., Vedaldi, A. and Zisserman, A., 2015. Deep face recognition

    Google Scholar 

  34. Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J.M. and Herrera, F., 2017. Robust classification of different fingerprint copies with deep neural networks for database penetration rate reduction. arXiv preprint arXiv:1703.07270

    Google Scholar 

  35. Schroff, F., Kalenichenko, D. and Philbin, J., 2015. Facenet: a unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 815-823)

    Google Scholar 

  36. Shehu, Y.I., Ruiz-Garcia, A., Palade, V., James, A.: Detection of fingerprint alterations using deep convolutional neural networks. In: International conference on artificial neural networks, pp. 51–60. Springer, Cham (2018, October)

    Google Scholar 

  37. Taigman, Y., Yang, M., Ranzato, M.A. and Wolf, L., 2014. Deepface: closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701–1708)

    Google Scholar 

  38. Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    CrossRef  Google Scholar 

  39. UNdata, “UNdata | explorer”, data.un.org, http://data.un.org/Explorer.aspx (accessed May 17, 2020)

  40. US Energy Information Administration (EIA), “Residential Energy Consumption Survey (RECS) – Data – US Energy Information Administration (EIA)”, eia.gov, https://www.eia.gov/consumption/residential/data/2015/ (accessed May 17, 2020)

  41. Wang, H., Huang, T., Liao, X., Abu-Rub, H., Chen, G.: Reinforcement learning in energy trading game among smart microgrids. IEEE Trans. Ind. Electron. 63(8), 5109–5119 (2016)

    CrossRef  Google Scholar 

  42. Yi, D., Lei, Z., Liao, S. and Li, S.Z., 2014. Learning face representation from scratch. arXiv preprint arXiv:1411.7923

    Google Scholar 

  43. Zhang, D., Han, X., Deng, C.: Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE Journal of Power and Energy Systems. 4(3), 362–370 (2018)

    CrossRef  Google Scholar 

  44. Zhou, S., Chen, Q. and Wang, X., 2010, June. HIT-OR3C: an opening recognition corpus for Chinese characters. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems (pp. 223–230)

    Google Scholar 

  45. Shrein, John M. “Fingerprint classification using convolutional neural networks and ridge orientation images.” In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), (pp. 1–8) IEEE (2017).

    Google Scholar 

  46. Ghaderi, Amir, Borhan M. Sanandaji, and Faezeh Ghaderi. “Deep forecast: Deep learning-based spatio-temporal forecasting.” arXiv preprint arXiv:1707.08110 (2017)

    Google Scholar 

  47. JHA, A. K., Ruwali, A., Prakash, K.B., Kanagachidambaresan, G.R.: Tensor flow basics, programming with tensor Flow, EIA/Springer innovations in communication and computing. https://doi.org/10.1007/978-3-030-57077-4_2

  48. Kanagachidambaresan, G. R., Manohar Vinoothna, G., Prakash, K.B.: Visualizations, programming with tensor flow, EIA/Springer innovations in communication and computing. https://doi.org/10.1007/978-3-030-57077-4_3

  49. Nagapawan, Y.V.R., Prakash, K.B., Kanagachidambaresan, G.R.: Convolution neural network, programming with tensor Flow, EIA/Springer innovations in communication and computing. https://doi.org/10.1007/978-3-030-57077-4_6

  50. Kanagachidambaresan, G.R., Ruwali, A., Banerjee, D., Prakash, K.B.: Recurrent neural network, programming with tensor flow, EIA/Springer innovations in communication and computing. https://doi.org/10.1007/978-3-030-57077-4_7

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, College of Engineering Trivandrum, Thiruvananthapuram, India

    Nahil Ahmed Hassan & Ajeesh Ramanujan

  2. Department of Electrical Engineering, College of Engineering Trivandrum, Thiruvananthapuram, India

    Abhigith Neil Abraham

Authors
  1. Nahil Ahmed Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abhigith Neil Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ajeesh Ramanujan
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. KL Deemed to be University, Vijayawada, AP, India

    Dr. Kolla Bhanu Prakash

  2. Electrical & Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar, Perak, Malaysia

    Dr. Ramani Kannan

  3. Kongu Engineering College, Perundurai, Tamil Nadu, India

    S.Albert Alexander

  4. Department of CSE, Vel Tech Rangarajan Dr Sagunthala R&D, Institute of Science and Technology, Chennai, Tamil Nadu, India

    G. R. Kanagachidambaresan

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hassan, N.A., Abraham, A.N., Ramanujan, A. (2021). Deep Learning Applications with Python. In: Prakash, K.B., Kannan, R., Alexander, S., Kanagachidambaresan, G.R. (eds) Advanced Deep Learning for Engineers and Scientists. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-66519-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66519-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66518-0

  • Online ISBN: 978-3-030-66519-7

  • eBook Packages: EngineeringEngineering (R0)

search

Navigation