DL technology uses multiple layers to represent the abstractions of data to build computational models. Deep learning differs from standard machine learning in terms of efficiency as the volume of data increases, discussed briefly in Section “ Why Deep Learning in Today's Research and Applications?”. The worldwide popularity of “Deep learning” is increasing day by day, which is shown in our earlier paper based on the historical data collected from Google trends. In terms of working domain, DL is considered as a subset of ML and AI, and thus DL can be seen as an AI function that mimics the human brain’s processing of data.
Many corporations including Google, Microsoft, Nokia, etc., study it actively as it can provide significant results in different classification and regression problems and datasets. Nowadays, DL technology is considered as one of the hot topics within the area of machine learning, artificial intelligence as well as data science and analytics, due to its learning capabilities from the given data. This is because deep networks, when properly trained, have produced significant success in a variety of classification and regression challenges. Deep learning became a prominent topic after that, resulting in a rebirth in neural network research, hence, some times referred to as “new-generation neural networks”.
, which was based on the concept of artificial neural network (ANN).
After that, in 2006, “Deep Learning” (DL) was introduced by Hinton et al. While neural networks are successfully used in many applications, the interest in researching this topic decreased later on. Multilayer perceptron networks trained by “Backpropagation” type algorithms, self-organizing maps, and radial basis function networks were such innovative methods. 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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