Differences Between Deep And Machine Learning: A Picture Is Worth a 1000 White Papers
March 9, 2021
It is difficult to explain how AI and deep learning networks work, unless you have a background in IT or are actually designing them. Humans, however, are visual learners and Data Science Central shared a nifty graphic that sums up the concepts quite nicely: “Deep Learning Versus Machine Learning In One Picture.”
Machine learning networks are complex, in-depth algorithms that are designed to learn from data. When compared to deep learning networks, machine learning networks are like basic input/output commands, Machine learning networks are unique within their contained, local environment, but deep learning networks extend beyond uniqueness. They can operate in more than one dimension, communicate with more than environment, process data from many sources, and learn at a greater and faster rate.
Despite the differences, the image fails to answer basic questions about the networks. These questions include: which is cheaper to implement? Is one type of network better for certain situations or are they applicable to everything? How can they be altered if not applicable?
Complexity does not mean better, but keeping things simpler does not mean that either. The image does a great job at summing up the basics, but it does not do anything to answer practical application questions.
Whitney Grace, March 9, 2021
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