Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.
Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions.
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DOI：
10.1007/s42979-021-00815-1
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发表时间：
2021
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影响因子：
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通讯作者：
Sarker IH
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作者：
Sarker IH
深度学习（DL）是机器学习（ML）和人工智能（AI）的一个分支，目前被认为是当今第四次工业革命（4IR或工业4.0）的核心技术。由于其从数据中学习的能力，DL技术起源于人工神经网络（ANN），已成为计算环境中的热门话题，并广泛应用于医疗保健，视觉识别，文本分析，网络安全等各种应用领域。然而，建立一个适当的DL模型是一项具有挑战性的任务，由于动态的性质和变化，在现实世界中的问题和数据。此外，缺乏对核心的理解将DL方法变成了黑盒子机器，阻碍了标准级别的开发。本文对深度学习技术进行了结构化和全面的介绍，包括考虑各种类型的现实任务（如监督或无监督）的分类。在我们的分类中，我们考虑了深度网络的监督或区分学习，无监督或生成学习以及混合学习和其他相关学习。我们还总结了可以使用深度学习技术的实际应用领域。最后，我们指出了十个潜在的未来一代DL建模的研究方向。总的来说，本文旨在绘制DL建模的大图，可用作学术界和行业专业人士的参考指南。
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.