Review of Deep Learning Models from Convolution Neural Networks to Transformers

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Sandeep Maan; Gian Devi

Review of Deep Learning Models from Convolution Neural Networks to Transformers

Authors: Sandeep Maan, Gian Devi

Unique Paper ID: 160893
Volume: 10
Issue: 1
PageNo: 1331-1335

Keywords: Artificial Intelligence Machine Leaning Deep Learning Convolution Neural Networks (CNN) Recurrent Neural Networks (RNN) Attention Transformers Large Language Models (LLM)

Abstract:
Generative Artificial Intelligence has become synonym for Artificial Intelligence. Specifically, success of Large Language Model (LLM) is going to make long lasting disruptive effect. Application like ChatGPT [1] by OpenAI, BARD by Google are believed to change the human-machine relation in coming years. All these can be attributed to the developments in the field of deep learning during last decade. Things started with Convolutional Neural Networks (CNN) and advancement of GPUs that has made as lasting effect in the field of image processing. In this paper authors have reviewed features and limitation of most three most popular deep learning models viz. Convolution Neural Networks (CNN), Recurrent Neural Networks (RNN) and Transformer model. A systemic study of factors leading to the development of large language models is also presented.

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 1
PageNo: 1331-1335

Review of Deep Learning Models from Convolution Neural Networks to Transformers

Available:https://ijirt.org/article?manuscript=160893

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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