A Comprehensive Survey of Deep Learning Architectures: Conducting a Thorough Examination of Various Deep Learning Architectures, Their Applications, and Advancements
Keywords:
Deep learning, neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, image recognition, speech recognition, autonomous driving, attention mechanisms, transformer models, self-supervised learningAbstract
Deep learning has emerged as a powerful approach in artificial intelligence, revolutionizing various fields such as computer vision, natural language processing, and robotics. This paper provides a comprehensive survey of deep learning architectures, focusing on their applications and recent advancements. We begin by discussing the fundamentals of deep learning and then delve into various popular architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). We also explore their applications across different domains, such as image recognition, speech recognition, and autonomous driving. Furthermore, we discuss recent advancements in deep learning, such as attention mechanisms, transformer models, and self-supervised learning. Finally, we present future directions and challenges in the field of deep learning.
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