IDEAS home Printed from https://ideas.repec.org/a/sae/jedbes/v45y2020i2p227-248.html
   My bibliography  Save this article

Deep Learning With TensorFlow: A Review

Author

Listed:
  • Bo Pang
  • Erik Nijkamp
  • Ying Nian Wu

    (UCLA)

Abstract

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.

Suggested Citation

  • Bo Pang & Erik Nijkamp & Ying Nian Wu, 2020. "Deep Learning With TensorFlow: A Review," Journal of Educational and Behavioral Statistics, , vol. 45(2), pages 227-248, April.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:2:p:227-248
    DOI: 10.3102/1076998619872761
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.3102/1076998619872761
    Download Restriction: no
    File URL: https://libkey.io/10.3102/1076998619872761?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Md. Tarek Hasan & Md. Al Emran Hossain & Md. Saddam Hossain Mukta & Arifa Akter & Mohiuddin Ahmed & Salekul Islam, 2023. "A Review on Deep-Learning-Based Cyberbullying Detection," Future Internet, MDPI, vol. 15(5), pages 1-47, May.
    2. Peng Zhang & Huize Ren & Xiaobin Dong & Xuechao Wang & Mengxue Liu & Ying Zhang & Yufang Zhang & Jiuming Huang & Shuheng Dong & Ruiming Xiao, 2023. "Understanding and Applications of Tensors in Ecosystem Services: A Case Study of the Manas River Basin," Land, MDPI, vol. 12(2), pages 1-23, February.
    3. Vishakha Sood & Reet Kamal Tiwari & Sartajvir Singh & Ravneet Kaur & Bikash Ranjan Parida, 2022. "Glacier Boundary Mapping Using Deep Learning Classification over Bara Shigri Glacier in Western Himalayas," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    4. Jabir, Brahim & Moutaouakil, Khalid El & Falih, Noureddine, 2023. "Developing an Efficient System with Mask R-CNN for Agricultural Applications," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 15(1), January.
    5. Xianbin Wang & Yuqi Zhao & Weifeng Li, 2023. "Recognition of Commercial Vehicle Driving Cycles Based on Multilayer Perceptron Model," Sustainability, MDPI, vol. 15(3), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jedbes:v:45:y:2020:i:2:p:227-248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.