The Evolution of Deep Learning: Models, Applications, and Future Directions

Abstract

This paper presents a comprehensive survey of deep learning (DL) models, systematically categorizing them from foundational architectures such as multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to advanced frameworks like transformers, generative adversarial networks (GANs), large language models (LLMs), and large multimodal models (LMMs). The survey reviews core mechanisms, architectures, and applications across domains, including computer vision, natural language processing, healthcare, and robotics. It highlights emerging trends such as efficient model design, transfer learning, and multimodal integration while addressing key challenges including interpretability, scalability, generalization, and robustness. By critically analyzing the strengths, limitations, and future directions of DL, this work serves as a comprehensive reference for researchers and practitioners seeking to navigate the evolving deep learning landscape.