Abstract
Data analytics include applications like object recognition, video surveillance, self-driving cars, and tracking objects. These applications are advanced analytical applications and machine learning-based classification models cannot be used. Advanced classification models include neural networks as the basis for analytics. Neural networks consist of input, hidden layers, and the output. TensorFlow is one of the analytical tools that help in developing advanced analytical applications on image and video analytics. It helps to build neural networks with the required number of hidden layers for the model. In this chapter, an overview of TensorFlow and its working is discussed. Image analytics with MNIST data is discussed first where the handwritten digits are recognized using a TensorFlow model. Later, the case studies on spam classification and question classification are revisited once again that were a part of machine learning. The main aim of the chapter is to discuss the TensorFlow and its applications in analytics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
MartÃn, A., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., & Corrado, G. S. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. URL: http://tensorflow.org/. Software available from: http://tensorow.org.
Glauner, P. (2016). Deep learning on big data sets in the cloud with apache spark and google tensorflow.
MNIST data: https://archive.ics.uci.edu/ml/databases/mnist/.
Mehta, P., Dorkenwald, S., Zhao, D., Kaftan, T., Cheung, A., Balazinska, M., … & AlSayyad, Y. (2017). Comparative evaluation of big-data systems on scientific image analytics workloads. Proceedings of the VLDB Endowment, 10(11), 1226–1237.
Berral-GarcÃa, J. L. (2016, July). A quick view on current techniques and machine learning algorithms for big data analytics. In 2016 18th international conference on transparent optical networks (ICTON) (pp. 1–4). IEEE.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Srinivasa, K.G., G. M., S., H., S. (2018). Advanced Analytics with TensorFlow. In: Network Data Analytics. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-77800-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-77800-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77799-3
Online ISBN: 978-3-319-77800-6
eBook Packages: Computer ScienceComputer Science (R0)