A Comparative Analysis of Deep Learning Frameworks and Libraries

Authors

  • M. Nagabhushana Rao Professor, Dept of IT, Vidya Jyothi Institute of Technology, Hyderabad

Keywords:

Deep learning frameworks, comparative analysis, performance evaluation, benchmark datasets, TensorFlow

Abstract

Deep learning has become a popular approach for solving complex problems in various fields, including image recognition, natural language processing, and speech recognition. As a result, numerous deep learning frameworks and libraries have been developed, each with its unique strengths and weaknesses. Choosing the right framework and library for a given application is essential for achieving optimal performance and accuracy. This study aims to provide a comparative analysis of deep learning frameworks and libraries based on their ease of use, computational efficiency, flexibility, and performance. The study evaluates six popular deep-learning frameworks and libraries, including TensorFlow, Keras, PyTorch, Caffe, MXNet, and Theano. The evaluation process includes the implementation of deep learning models using each framework, training, and testing on benchmark datasets, and collecting evaluation metrics. The study uses several benchmark datasets, including CIFAR-10, ImageNet, and MNIST. The study compares the evaluated deep learning frameworks and libraries in terms of their ease of use, computational efficiency, flexibility, and performance. The study also discusses the impact of the evaluated deep learning frameworks and libraries on the performance and accuracy of the developed models, highlighting the trade-offs and limitations of each framework. The results show that TensorFlow and PyTorch are the most popular and widely used frameworks due to their flexibility, ease of use, and strong community support. This study has several implications for practitioners in the field of deep learning, highlighting the importance of the selection of the appropriate framework and library for the development of successful models. The study also contributes new insights and knowledge to the field of deep learning and suggests future research directions for improving and extending the research in new directions. Overall, this study provides valuable information for researchers and practitioners seeking to evaluate and select the best deep-learning framework and library for their specific needs.

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References

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Block diagram for data processing

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Published

27.01.2023

How to Cite

Rao, M. N. . (2023). A Comparative Analysis of Deep Learning Frameworks and Libraries. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 337–342. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2707

Issue

Vol. 11 No. 2s (2023)

Section

Research Article

License

Copyright (c) 2023 M. Nagabhushana Rao

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