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

被引：949
作者：
Sarker I.H. [1 ,2 ]
机构：
[1] Swinburne University of Technology, Melbourne, 3122, VIC
[2] Chittagong University of Engineering & Technology, Chittagong
来源：
SN Computer Science | 2021年 / 2卷 / 6期
关键词：
Artificial intelligence; Artificial neural network; Deep learning; Discriminative learning; Generative learning; Hybrid learning; Intelligent systems;
D O I：
10.1007/s42979-021-00815-1
中图分类号：
学科分类号：
摘要：
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. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
引用
收藏
相关论文
共 135 条
[11]  
Bu F., Wang X., A smart agriculture iot system based on deep reinforcement learning, Futur Gener Comput Syst, 99, pp. 500-507, (2019)
[12]  
Chang W.-J., Chen L.-B., Hsu C.-H., Lin C.-P., Yang T.-C., A deep learning-based intelligent medicine recognition system for chronic patients, IEEE Access, 7, pp. 44441-44458, (2019)
[13]  
Chaudhari S., Mithal V., Polatkan G., Ramanath R., An Attentive Survey of Attention Models. Arxiv Preprint Arxiv, 1904, (2019)
[14]  
Chaudhuri N., Gupta G., Vamsi V., Bose I., On the platform but will they buy? Predicting customers’ purchase behavior using deep learning, Decis Support Syst, (2021)
[15]  
Chen D., Wawrzynski P., Lv Z., Cyber security in smart cities: A review of deep learning-based applications and case studies, Sustain Cities Soc, (2020)
[16]  
Cho K., Van M.B., Gulcehre C., Bahdanau D., Bougares F., Schwenk H., Bengio Y., Learning Phrase Representations Using Rnn Encoder-Decoder for Statistical Machine Translation. Arxiv Preprint Arxiv, 1406, (2014)
[17]  
Chollet F., Xception: Deep learning with depthwise separable convolutions, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251-1258, (2017)
[18]  
Chung J., Gulcehre C., Cho K.H., Bengio Y., Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Arxiv Preprint Arxiv, 1412, (2014)
[19]  
Coelho I.M., Coelho V.N., da Eduardo J., Luz S., Ochi L.S., Guimaraes F.G., Rios E., A gpu deep learning metaheuristic based model for time series forecasting, Appl Energy, 201, pp. 412-418, (2017)
[20]  
Da'u A., Salim N., Recommendation system based on deep learning methods: a systematic review and new directions, Artif Intel Rev, 53, 4, pp. 2709-2748, (2020)
12345678910