Deep Learning Architectures: A Comprehensive Review of Models and Applications
Keywords:
Computer Vision, Natural Language Processing (NLP), Chatbots, Autonomous SystemsAbstract
Deep Learning (DL) has become a revolutionary paradigm in artificial intelligence, with the capacity to learn features in hierarchies and perform well in various complex data-driven problems. This review article gives a detailed discussion of some key deep learning architectures, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformers, Autoencoders, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (DRL). This paper explores their structural bases, learning processes as well as their unique features. It also discusses their use in the field of computer vision, natural language processing, autonomous systems, finance, medical research, and scientific research. The review summarizes the current literature to emphasize on architectural developments, hybrid structures, and application-specifications. The major issues of scaling, computational complexity, interpretability, bias, and data constraints are also addressed. The paper offers an organized insight into the deep learning models and gives a perspective of the upcoming research to develop efficient, explainable, and robust intelligent systems.
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