A SYSTEMATIC ASSESSMENT OF DEEP LEARNING APPLICATIONS AND CHALLENGES: FROM HYPE TO REALITY
Ashalatha P R
Lecturer in Computer Science & Engg., Government Polytechnic, K.R.Pete, Karnataka, India
Receiving Date:
2016-01-21
Acceptance Date:
2016-03-09
Publication Date:
2016-03-20
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Abstract
In recent years, deep learning has garnered significant attention and excitement within the research community and industry alike. This paper presents a systematic assessment of the applications and challenges associated with deep learning, aiming to provide a comprehensive understanding of its transition from a hyped concept to practical reality. We delve into the various domains where deep learning has been applied, highlighting its successes and limitations. By examining the evolution of deep learning techniques, we analyze the factors that have contributed to its success and explore the obstacles that hinder its widespread adoption. This assessment offers a balanced perspective on the current state of deep learning, shedding light on both its transformative potential and the pragmatic considerations that need to be addressed for its continued advancement.
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Keywords:
Deep learning; artificial neural networks; hype; reality; machine learning; data analysis; image recognition; natural language processing; pattern recognition; feature learning; neural architecture
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