Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions

By Iqbal H. Sarker. SN Computer Science, 2021

2,312 citations recorded by the source corpus.

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). 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. However, building an appropriate DL model is a challenging task, due to the dynamic nature and variations in real-world problems and data. Moreover, the lack of core understanding turns DL methods into black-box machines that hamper development at the standard level. 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. 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. We also summarize real-world application areas where deep learning techniques can be used. Finally, we point out ten potential aspects for future generation DL modeling with research directions. 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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/db6ad6ded1cfa26fdc7437f27fb823ec533e96fe

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Citations in the corpus

AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, background): “• Nowadays, data are considered as the most valuable resource in the world and various machine learning [81] and deep learning [80] techniques are used to learn from data or past experience, which automates analytical model building.”
An advanced deep learning models-based plant disease detection: A review of recent research (2023, background): “Iqbal (Sarker, 2021) popularized the term “Deep Learning” in a 2006 Science article (DL).”
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, background): “The Multi-Layer Perceptron (MLP) model is a type of feedforward artificial neural network (ANN) that serves as a foundation architecture for deep learning or deep neural networks (DNNs) [7].”
Deep learning in drug discovery: an integrative review and future challenges (2022, neutral): “We’ll review the various deep learning models in this section (Sarker 2021).”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “It has become a popular topic in the computing world, with applications in healthcare, visual recognition, text analytics, cybersecurity, and a variety of other ﬁelds [171].”
Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions (2023, background): “Deep neural networks are effective in hybrid learning as well as supervised, unsupervised, and reinforcement learning [56].”