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A Decade of Deep Learning: A Survey on The Magnificent Seven

A Decade of Deep Learning: A Survey on The Magnificent Seven

Authors :
Azizov, Dilshod
Manzoor, Muhammad Arslan
Bojkovic, Velibor
Wang, Yingxu
Wang, Zixiao
Iklassov, Zangir
Zhao, Kailong
Li, Liang
Liu, Siwei
Zhong, Yu
Liu, Wei
Liang, Shangsong
Publication Year :
2024

Abstract

Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across diverse domains. At the heart of these developments lie multi-layered neural network architectures that excel at automatic feature extraction, leading to significant improvements in machine learning tasks. To demystify these advances and offer accessible guidance, we present a comprehensive overview of the most influential deep learning algorithms selected through a broad-based survey of the field. Our discussion centers on pivotal architectures, including Residual Networks, Transformers, Generative Adversarial Networks, Variational Autoencoders, Graph Neural Networks, Contrastive Language-Image Pre-training, and Diffusion models. We detail their historical context, highlight their mathematical foundations and algorithmic principles, and examine subsequent variants, extensions, and practical considerations such as training methodologies, normalization techniques, and learning rate schedules. Beyond historical and technical insights, we also address their applications, challenges, and potential research directions. This survey aims to serve as a practical manual for both newcomers seeking an entry point into cutting-edge deep learning methods and experienced researchers transitioning into this rapidly evolving domain.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2412.16188
Document Type :
Working Paper