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About Three Reasons That You Should NOT Use Deep Learning - by George Seif - Towards Data Science
(1) Deep learning requires large datasets to be effective, but many applications do not have access to large datasets, limiting its usefulness. (2) Deep learning models are complex and expensive to develop, requiring significant resources, time, and specialized expertise that may not be feasible for some problems. (3) Deep learning models are difficult to interpret, functioning as "black boxes," which can limit their applicability when interpretability of results is important. Classical machine learning methods may be preferable in some cases due to their easier interpretability.
Detailed Information
| Author: | ['vaskore'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 1 |
| Language: | English |
| Format: | |
| Price: | FREE |
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