Deep Learning

Viswanathan, Ganesh and Samdani, Gaurav and Dixit, Yawal and Gopalan, Ranjith (2025) Deep Learning. World Journal of Advanced Engineering Technology and Sciences, 14 (3). pp. 512-527. ISSN 2582-8266

Deep learning has revolutionized artificial intelligence by enabling machines to learn complex patterns from vast amounts of data. This white paper explores the fundamental principles of deep learning, including neural network architectures, training methodologies, and key advancements such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. We discuss applications across various domains, including computer vision, natural language processing, healthcare, and finance, highlighting real-world use cases and breakthroughs. Additionally, we examine the challenges of deep learning, such as data requirements, model interpretability, and computational constraints, along with emerging trends in model efficiency and responsible AI. This paper aims to provide insights into the current state of deep learning and its future trajectory, helping researchers and industry professionals navigate the rapidly evolving AI landscape.