Exploring Deep Learning: Breakthroughs, Limitations, and Future Directions

Deep learning has revolutionized artificial intelligence, enabling major advances in computer vision, natural language processing, and biomedical imaging. This review examines core architectures—convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative models—alongside their theoretical foundations and practical applications. By synthesizing existing literature and case studies, the paper highlights both strengths and challenges, including scalability, interpretability, and computational demands. Emerging paradigms such as diffusion models and hybrid generative approaches are identified as promising solutions to current limitations. While the study acknowledges its reliance on secondary sources and a restricted scope of reviewed works, it emphasizes the need for future research incorporating empirical validation and larger datasets. The review ultimately provides researchers and practitioners with a structured perspective on the evolution of deep learning, its unresolved issues, and its potential to shape the next generation of intelligent systems

Artificial Intelligence, Neural Architectures, Scalability, Interpretability, Computational Efficiency

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