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Article summary:
1. Deep Learning (DL) is a subset of Machine Learning and Artificial Intelligence, which uses multiple layers to represent the abstractions of data to build computational models.
2. DL technology is widely used in many fields such as healthcare, sentiment analysis, natural language processing, visual recognition, business intelligence, cybersecurity, and more.
3. This paper presents a structured overview on DL techniques considering the variations in real-world problems and tasks by taking into account three major categories: supervised learning, unsupervised learning and hybrid learning.
Article analysis:
The article provides an overview of deep learning techniques and their applications in various fields. The authors provide a comprehensive view on DL techniques by taking into account three major categories: supervised learning, unsupervised learning and hybrid learning. The article also discusses the advantages of deep learning over other machine learning algorithms such as its efficiency when dealing with large datasets.
The article appears to be reliable as it provides detailed information about deep learning techniques and their applications in various fields. Furthermore, the authors provide a comprehensive view on DL techniques by taking into account three major categories: supervised learning, unsupervised learning and hybrid learning. Additionally, the article cites relevant sources to support its claims which adds to its credibility.
However, there are some potential biases that should be noted in this article. For example, the authors do not discuss any potential risks associated with deep learning or any possible drawbacks that could arise from using it for certain applications. Additionally, they do not explore any counterarguments or present both sides equally when discussing the advantages of deep learning over other machine learning algorithms. Furthermore, there is no mention of any promotional content or partiality in the article which could have added further depth to its discussion on deep learning techniques and their applications in various fields.
In conclusion, while this article appears to be reliable overall due to its detailed discussion on deep learning techniques and their applications in various fields as well as citing relevant sources to support its claims; there are some potential biases that should be noted such as not exploring any counterarguments or presenting both sides equally when discussing the advantages of deep learning over other machine learning algorithms as well as not mentioning any potential risks associated with it or possible drawbacks that could arise from using it for certain applications.