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Overview of deep learning | IEEE Conference Publication | IEEE Xplore
Source: ieeexplore-ieee-org-443.wvpn.hrbeu.edu.cn
Article summary:
1. Deep learning has achieved great success in many fields, such as computer vision and natural language processing.
2. This paper introduces some advanced neural networks of deep learning and their applications, as well as the limitations and prospects of deep learning.
3. The research of artificial neural network began from 1940s, and now it is a prevalent field of machine learning due to its strong learning ability and practicability.
Article analysis:
The article provides an overview of deep learning, discussing its history, current state-of-the-art models, frameworks, applications, limitations and prospects. The article is written in a clear and concise manner that is easy to understand for readers with varying levels of knowledge on the subject matter. The article also provides references to support its claims which adds to its trustworthiness and reliability.
However, there are some potential biases present in the article that should be noted. For example, the article does not provide any counterarguments or explore any possible risks associated with deep learning which could lead to a one-sided reporting of the topic. Additionally, there are some unsupported claims made throughout the article that lack evidence or further explanation which could lead readers to draw incorrect conclusions about certain aspects of deep learning. Furthermore, there are some points that are missing from consideration such as ethical implications or potential misuse of deep learning technology which could have been explored further in order to provide a more comprehensive overview of the topic.
In conclusion, while this article provides an informative overview on deep learning with references to support its claims, it should be noted that there are potential biases present in the form of one-sided reporting and unsupported claims which could lead readers to draw incorrect conclusions about certain aspects of deep learning if they are not aware of them beforehand.