Summary of A Survey on State-of-the-art Deep Learning Applications and Challenges, by Mohd Halim Mohd Noor and Ayokunle Olalekan Ige
A Survey on State-of-the-art Deep Learning Applications and Challenges
by Mohd Halim Mohd Noor, Ayokunle Olalekan Ige
First submitted to arxiv on: 26 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper reviews state-of-the-art deep learning models across multiple domains, including computer vision, natural language processing, time series analysis, pervasive computing, and robotics. Building on previous studies, this study aims to provide a comprehensive overview of the most effective models in each domain, highlighting their key features and applications. The review also covers fundamental concepts, various model types, and prominent convolutional neural network architectures. Additionally, challenges and future directions are discussed to offer insights for future researchers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how deep learning can be used to solve complex problems in different areas like computer vision, language, time series analysis, and robotics. Deep learning is a way to make computers learn from data without being explicitly programmed. The paper reviews the best approaches for each area and what makes them successful. It also explains the basics of deep learning and some common models. Finally, it talks about the challenges that remain in this field. |
Keywords
* Artificial intelligence * Deep learning * Natural language processing * Neural network * Time series
