A Survey of Deep Learning Techniques: Applications Across Industries and Ethical Considerations

Deep learning, a transformative subset of machine learning, has revolutionized artificial intelligence by enabling the automatic extraction of complex patterns from large-scale data. This survey comprehensively reviews deep learning techniques—including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transformer Models, and Deep Reinforcement Learning (DRL)—and their applications across industries such as healthcare, finance, manufacturing, and environmental science. We examine their foundational architectures, training methodologies, and key roles in computer vision, natural language processing (NLP), autonomous systems, and smart industry. Additionally, this survey discusses challenges in interpretability, data efficiency, ethics, and societal impact. By exploring foundational methods and emerging trends like self-supervised learning, transfer learning, and explainable AI (XAI), it serves as a valuable resource for researchers and practitioners applying deep learning to real- world scenarios.