Deep learning, a branch of artificial intelligence, is a computational model that uses multiple layers of interconnected units (neurons) to learn intricate patterns and representations directly from raw input data. Empowered by this learning capability, it has become a powerful tool for solving complex problems and is the core driver of many groundbreaking technologies and innovations. Building a deep learning model is a challenging task due to the algorithm`s complexity and the dynamic nature of real-world problems. Several studies have reviewed deep learning concepts and applications. However, the studies mostly focused on the types of deep learning models and convolutional neural network architectures, offering limited coverage of the state-of-the-art of deep learning models and their applications in solving complex problems across different domains. Therefore, motivated by the limitations, this study aims to comprehensively review the state-of-the-art deep learning models in computer vision, natural language processing, time series analysis and pervasive computing. We highlight the key features of the models and their effectiveness in solving the problems within each domain. Furthermore, this study presents the fundamentals of deep learning, various deep learning model types and prominent convolutional neural network architectures. Finally, challenges and future directions in deep learning research are discussed to offer a broader perspective for future researchers.

中文翻译：

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深度学习和最先进应用的调查

深度学习是人工智能的一个分支，是一种计算模型，它使用多层互连单元（神经元）直接从原始输入数据中学习复杂的模式和表示。在这种学习能力的支持下，它已成为解决复杂问题的强大工具，并且是许多突破性技术和创新的核心驱动力。由于算法的复杂性和现实世界问题的动态性质，构建深度学习模型是一项具有挑战性的任务。一些研究回顾了深度学习的概念和应用。然而，这些研究主要集中在深度学习模型和卷积神经网络架构的类型上，对深度学习模型的最新技术及其在解决不同领域的复杂问题中的应用的覆盖范围有限。因此，出于局限性，本研究旨在全面回顾计算机视觉、自然语言处理、时间序列分析和普适计算领域最先进的深度学习模型。我们强调模型的关键特征及其在解决每个领域内的问题时的有效性。此外，这项研究还介绍了深度学习的基础知识、各种深度学习模型类型和著名的卷积神经网络架构。最后，讨论了深度学习研究的挑战和未来方向，为未来的研究人员提供更广阔的视野。