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2024 | OriginalPaper | Buchkapitel

Application of Machine Learning and Deep Learning in High Performance Computing

verfasst von : Manikandan Murugaiah

Erschienen in: High Performance Computing in Biomimetics

Verlag: Springer Nature Singapore

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Abstract

This chapter explores the fascinating intersection of biology and computer science, where nature’s design principles are harnessed to solve complex computational problems. This chapter provides an overview of bio-inspired computing techniques, including genetic algorithms, neural networks, swarm intelligence, and cellular automata. It goes into the core concepts of each approach, highlighting their biological counterparts and demonstrating their applications across various domains. Furthermore, this chapter discusses the evolution of bio-inspired algorithms, emphasizing their adaptation to contemporary computing paradigms such as machine learning and artificial intelligence. It examines how these algorithms have been employed to address real-world challenges, ranging from optimization problems and pattern recognition to robotics and autonomous systems. In addition to theoretical insights, the chapter offers practical guidance on implementing bio-inspired algorithms, including algorithmic design considerations and the integration of bio-inspired approaches with traditional computing methods. It also discusses the ethical and societal implications of bio-inspired computing, touching upon topics like algorithm bias and data privacy.

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Vorheriges Kapitel Application of Cuckoo Search Algorithm in Bio-inspired Computing Using HPC Platform

Nächstes Kapitel The Future of High Performance Computing in Biomimetics and Some Challenges

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Titel: Application of Machine Learning and Deep Learning in High Performance Computing
verfasst von: Manikandan Murugaiah
Verlag: Springer Nature Singapore
Buch: High Performance Computing in Biomimetics
Print ISBN: 978-981-9710-16-4

Electronic ISBN: 978-981-9710-17-1

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-1017-1_14

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