  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.
