Currently, researchers of both (academic and industry) have proposed a lot of applications of deep-learning in different fields. Still, much of this research remains prototypes and away from the production stage. Deploying deep-learning models remains a noteworthy challenge. My question is; what are the most effective strategies to improve the deployment of deep-learning models, in order to make them efficient for real applications?
Dear Mabrook,
Deep learning efficiently analyzes and predicts unstructured data. For the deployment in a real application, you must optimize the hyperparameters.
Dear Mabrook Alrakhami ,
If you take a look at the literature and publications about Artificial Intelligence since the 21st century, you can’t ignore Deep Learning. It is a subset of Machine Learning that involves learning a hierarchy of features to gain meaningful insights from a complex input space.
https://nanonets.com/blog/10-best-practices-deep-learning/
Regards,
Shafagat
Deep learning is a class of machine learning algorithms that (pp199–200) uses multiple layers to progressively extract higher-level features: see the book "Decision Control, Management, and Support in ... "- Amazon
example: "Renewable Resources and Value Based Complex Forest Management, Encyclopedia of Information Science and Technology, Fifth Edition"-Researchgate
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