Summary of A Decade Of Deep Learning: a Survey on the Magnificent Seven, by Dilshod Azizov et al.
A Decade of Deep Learning: A Survey on The Magnificent Seven
by Dilshod Azizov, Muhammad Arslan Manzoor, Velibor Bojkovic, Yingxu Wang, Zixiao Wang, Zangir Iklassov, Kailong Zhao, Liang Li, Siwei Liu, Yu Zhong, Wei Liu, Shangsong Liang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep learning has revolutionized artificial intelligence in the past decade, enabling breakthroughs across various domains. The paper presents a comprehensive overview of influential deep learning algorithms, including Residual Networks, Transformers, Generative Adversarial Networks, Variational Autoencoders, Graph Neural Networks, Contrastive Language-Image Pre-training, and Diffusion models. This medium-difficulty summary delves into the mathematical foundations, algorithmic principles, and practical considerations of these architectures, as well as their applications, challenges, and potential research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning has changed artificial intelligence a lot over the past decade! The paper explains how different algorithms work together to make amazing things happen. It talks about some important ones like Residual Networks, Transformers, and more. This summary is easy to understand because it doesn’t use any technical words that you might not know. |
Keywords
» Artificial intelligence » Deep learning
