Deep learning has revolutionized artificial intelligence by enabling machines to learn hierarchically from data with minimal human intervention. Neural networks, inspired by the human brain’s structure, form the foundation of this paradigm shift, processing information through interconnected layers of artificial neurons to extract complex patterns from data. These architectures have transformed numerous domains including computer vision, natural language processing, and specialized applications such as autonomous vehicles and drug discovery. Despite remarkable achievements, significant challenges persist in interpretability, computational requirements, and data dependencies. Solutions including interpretable AI techniques, model compression, and transfer learning are actively addressing these limitations. The evolution of neural network designs, training methodologies, and optimization approaches continues to expand the capabilities and applications of deep learning while raising important considerations about ethics, sustainability, and accessibility.
Keywords: Neural Networks, computational efficiency, deep learning architectures, gradient descent optimization, model interpretability
