Abstract
The traditional algorithms of artificial intelligence and neural networks have many limitations to process big data in real time. Therefore, the researchers introduce the concept of deep learning to address the aforementioned challenge. However, big data analytics required a process consists of various steps where in each step an algorithm or a bunch of algorithm can be used. This chapter explains the role of machine learning in processing big data to meet various applications and users’ demands in real time. Similarly, various techniques of deep learning are studied to show how they can be used to address various challenges and issues of big data. Similarly, other similar techniques such as transfer learning are also discussed to support the study of deep learning.
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Abbreviations
- CNN:
-
Convolutional neural network
- DBN:
-
Deep belief network
- GPU:
-
Graphical processing unit
- RBM:
-
Restricted Boltzmann machine
- DSN:
-
Deep stacking network
- RFID:
-
Radio frequency identification
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Talha, M., Ali, S., Shah, S., Khan, F.G., Iqbal, J. (2019). Integration of Big Data and Deep Learning. In: Deep Learning: Convergence to Big Data Analytics. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-13-3459-7_4
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DOI: https://doi.org/10.1007/978-981-13-3459-7_4
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