International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Deep Learning: Concepts, Architectures, Workflow, Applications and Future Directions

Author(s) Jayeshkumar Madhubhai Patel
Country India
Abstract In recent years, deep learning (DL) has evolved into a statistical method in machine learning (ML) that produces surprising results in complex tasks, similar to human performance. Deep learning technology, derived from artificial neural networks (ANN), is a major advance in computer science because it allows learning from data. The ability to learn large amounts of data is one of the benefits of deep learning. In recent years, the field of higher education has grown rapidly and has been successfully used in many cultural fields. Deep learning outperforms well-known machine learning methods in a variety of fields, including cyber security, natural language processing, bioinformatics, robotics, and medical data tracking and management. To provide a better starting point from which to develop a comprehensive understanding of deep learning, this article also aims to take a closer look at even the most important aspects of deep learning, including current developments. In addition, this article highlights the importance of deep learning and explores different deep learning methods and networks.
Keywords Deep learning, Machine learning, Convolution neural network (CNN), Deep neural network architectures.
Field Engineering
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-12-31
Cite This Deep Learning: Concepts, Architectures, Workflow, Applications and Future Directions - Jayeshkumar Madhubhai Patel - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.11497
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.11497
Short DOI https://doi.org/gtbtcc
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