A Survey on State-of-the-art Deep Learning Applications and Challenges

TL;DR

This survey comprehensively reviews the latest deep learning models across multiple domains, highlighting their features, effectiveness, challenges, and future research directions in the field.

Contribution

It provides an extensive overview of state-of-the-art deep learning models and applications beyond traditional CNN architectures, covering diverse domains like vision, NLP, and robotics.

Findings

Deep learning models are effective in various complex tasks.

Challenges include model complexity and dynamic real-world problems.

Future directions involve addressing these challenges and exploring new architectures.

Abstract

Deep learning, a branch of artificial intelligence, is a data-driven method that uses multiple layers of interconnected units or 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 challenging 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 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, and robotics. 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.