Abstract
Deep learning has emerged as one of the most transformative paradigms in artificial intelligence, enabling machines to solve problems once considered exclusively human in complexity. The evolution from simple feedforward neural networks to advanced architectures like convolutional neural networks, recurrent neural networks, transformers, and graph neural networks has fundamentally altered computational problem solving. These advancements have made it possible to achieve breakthroughs in fields such as natural language processing, computer vision, medical diagnostics, financial forecasting, and autonomous systems. Deep learning architectures have evolved not just in terms of computational efficiency but also in terms of cognitive sophistication, mimicking aspects of human reasoning and perception. The integration of attention mechanisms, self-supervised learning, and hybrid architectures has pushed the boundaries of what machines can learn with minimal human intervention. This paper explores the major advancements in deep learning architectures, their structural innovations, and their applications in solving complex, real-world problems. Furthermore, it examines challenges related to interpretability, scalability, and ethical considerations, offering insights into the trajectory of future deep learning research.
Keywords
Deep Learning
Artificial Intelligence
Neural Networks
Convolutional Neural Networks
Transformers
Complex Problem Solving
Self-Supervised Learning
Machine Learning
Computational Intelligence
Artificial Neural Architecture
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