AI in Deep Learning: Advancements, Challenges, and Future Prospects

Swarda Jangam; Mohini Kate; Samruddhi Patil; Tanishka Pitale; Prof. Nikita kawase; Prof. Deepak K. Sharma

doi:10.36948/ijfmr.2023.v05i06.8901

AI in Deep Learning: Advancements, Challenges, and Future Prospects

Author(s)	Swarda Jangam, Mohini Kate, Samruddhi Patil, Tanishka Pitale, Prof. Nikita kawase, Prof. Deepak K. Sharma
Country	India
Abstract	Provide a concise summary of your research paper, including the main objectives, findings, and contributions. The advent of deep learning, a subfield of artificial intelligence, has ushered in a paradigm shift in the way machines perceive and interpret the world. At the core of this transformation lies deep neural networks, inspired by the human brain, which possess the remarkable capability to autonomously learn complex features from vast data repositories. This research paper explores the application of deep learning, with a particular focus on image recognition. This paper provides a comprehensive overview of deep learning's key concepts, including the architecture and training techniques of deep neural networks, exemplified by Convolutional Neural Networks (CNNs). Real-world applications of deep learning in image recognition are examined, illustrating its effectiveness in areas such as medical diagnostics, object detection, and autonomous vehicles. Moreover, we delve into recent advancements that have elevated deep learning to unprecedented heights, showcasing the state-of-the-art performance achieved in image recognition tasks. Challenges, such as data requirements and ethical concerns, are addressed, highlighting the need for responsible and equitable AI development.
Keywords	Business Intelligence, Data Analytics, Integration, Actionable Insights, Decision-making, Data-driven, Advanced Analytics, Predictive Analytics, Prescriptive Analytics, Data Integration, Business Strategy, Competitive Advantage, Data Mining, Data Warehousing.
Field	Engineering
Published In	Volume 5, Issue 6, November-December 2023
Published On	2023-11-11
Cite This	AI in Deep Learning: Advancements, Challenges, and Future Prospects - Swarda Jangam, Mohini Kate, Samruddhi Patil, Tanishka Pitale, Prof. Nikita kawase, Prof. Deepak K. Sharma - IJFMR Volume 5, Issue 6, November-December 2023. DOI 10.36948/ijfmr.2023.v05i06.8901
DOI	https://doi.org/10.36948/ijfmr.2023.v05i06.8901
Short DOI	https://doi.org/gs4xnw

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AI in Deep Learning: Advancements, Challenges, and Future Prospects

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