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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
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    Cited by:

    1. Baoyu Fan & Han Ma & Yue Liu & Xiaochen Yuan & Wei Ke, 2024. "KDTM: Multi-Stage Knowledge Distillation Transfer Model for Long-Tailed DGA Detection," Mathematics, MDPI, vol. 12(5), pages 1-19, February.
    2. 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.
    3. 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.
    4. 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.
    5. Filipe D. Campos & Tiago C. Sousa & Ramiro S. Barbosa, 2024. "Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM," Energies, MDPI, vol. 17(11), pages 1-19, May.
    6. 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.
    7. Hristo Ivanov Beloev & Stanislav Radikovich Saitov & Antonina Andreevna Filimonova & Natalia Dmitrievna Chichirova & Oleg Evgenievich Babikov & Iliya Krastev Iliev, 2024. "Prediction of Pipe Failure Rate in Heating Networks Using Machine Learning Methods," Energies, MDPI, vol. 17(14), pages 1-16, July.
    8. 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.
    9. Zachary K. Collier & Minji Kong & Olushola Soyoye & Kamal Chawla & Ann M. Aviles & Yasser Payne, 2024. "Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 241-267, April.

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