In the late 1980s, neural networks became a prevalent topic in the area of Machine Learning (ML) as well as Artificial Intelligence (AI), due to the invention of various efficient learning methods and network structures. Overall, this article aims to draw a big picture on DL modeling that can be used as a reference guide for both academia and industry professionals. Finally, we point out ten potential aspects for future generation DL modeling with research directions. We also summarize real-world application areas where deep learning techniques can be used. In our taxonomy, we take into account deep networks for supervised or discriminative learning, unsupervised or generative learning as well as hybrid learning and relevant others. This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised or unsupervised. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Due to its learning capabilities from data, DL technology originated from artificial neural network (ANN), has become a hot topic in the context of computing, and is widely applied in various application areas like healthcare, visual recognition, text analytics, cybersecurity, and many more. Deep learning (DL), a branch of machine learning (ML) and artificial intelligence (AI) is nowadays considered as a core technology of today’s Fourth Industrial Revolution (4IR or Industry 4.0).
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