Abstract
Artificial intelligence includes machine learning which further includes deep learning. Deep learning has humongous applications with extremely high accuracy. But deep learning models work for specific tasks only. Even running time is high from hours to days. They lack flexibility. They have no generic intelligence. They are computationally intensive. The focus of this paper is to train training data on recursive parallel processors (or supercomputers) for deep learning. This trained data would be helpful in fulfilling the mentioned challenges along with handling various application domains.
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Raheja, S., Chopra, R. (2020). Training Data on Recursive Parallel Processors for Deep Learning. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_5
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DOI: https://doi.org/10.1007/978-981-15-0372-6_5
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