Deep Learning: Foundational preparation for upskilling

https://doi.org/10.57040/icetis.vi.4

Authors

Keywords:

Artificial Intelligence, Cognitive process, Deep learning, Industry 4.0, Machine learning

Abstract

Emerging technologies are disrupting industries. The products and services are undergoing a quick revamp in their process of production. The service levels of engagement and delivery are radically varying upon digital transformations that are influencing the workflow. Artificial intelligence which marked the initiation with data science is leaping it's presence from machine learning to deep learning accommodating with multiple layers of non-polynomial algorithms handling unbounded width of information. Deep neural networks are with many-layer structure or even hidden layers, wherein the answers to the early layers pave way to simple and specific questions in a hierarchy that brings solutions to complex and abstract concepts. Artificial intelligence in machines includes machine learning, where they learn by experience and acquire skills without human involvement. Machine learning algorithms analyze the raw data and decide the relevant features, however in Deep learning the neural data networks improves and trains the data appropriately. Deep learning is an extended relation to machine learning, where neural networks algorithms learn from large amounts of data with artificial intelligence. The machine is capable to position it's performance based on various sensory inputs with a marked difference to the normal automation. To work in such an advanced setting, employability poses to be requirements for new skill sets. This paper attempts to cover the rudimentary requirements which are falling into the skills basket. Right from students to professionals the varying degree of skills acquisition can position them for greater demand in the job market.

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Published

2022-02-22

How to Cite

Umachandran, K. ., & Dinesh, D. . (2022). Deep Learning: Foundational preparation for upskilling . International Conference on Emerging Technology and Interdisciplinary Sciences, 29–39. https://doi.org/10.57040/icetis.vi.4