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Deep learning vs. machine learning - Azure Machine Learning | Microsoft Learn
Source: learn.microsoft.com
Article summary:
1. This article explains the differences between deep learning and machine learning, and how they fit into the broader category of artificial intelligence.
2. Deep learning is a subset of machine learning that uses artificial neural networks to learn from data. Machine learning is a subset of AI that uses techniques such as deep learning to improve at tasks.
3. Transfer learning is a technique that applies knowledge gained from solving one problem to a different but related problem, which can be used to shortcut the training process for deep learning models.
Article analysis:
The article provides an overview of deep learning vs. machine learning and how they fit into the broader category of artificial intelligence, as well as providing information on transfer learning and its use in deep learning models. The article appears to be reliable and trustworthy, providing clear definitions for each concept discussed and offering detailed explanations on their differences and similarities. The article also provides useful examples to illustrate its points, such as fraud detection, voice recognition, facial recognition, sentiment analysis, time series forecasting etc., which helps readers better understand the concepts discussed in the article.
The article does not appear to have any biases or one-sided reporting; it presents both sides equally by providing clear definitions for each concept discussed and offering detailed explanations on their differences and similarities. It also does not contain any unsupported claims or missing points of consideration; all claims are supported with evidence from reliable sources such as Azure Machine Learning and Microsoft Learn. Furthermore, there is no promotional content or partiality present in the article; it simply provides an overview of deep learning vs. machine learning without favoring either side over the other. Finally, possible risks are noted throughout the article; for example, it mentions that deep learning models require large amounts of training data and high-end compute resources (GPUs/TPUs), which can be costly if not available already.
All in all, this article appears to be reliable and trustworthy due to its lack of bias or one-sided reporting, supported claims with evidence from reliable sources, absence of promotional content or partiality towards either side being discussed, noting possible risks associated with using deep learning models etc., making it a good source for understanding the differences between deep learning vs machine learning in Azure Machine Learning