👉 DL (Deep Learning) research focuses on developing and refining artificial neural networks that can learn from large datasets with minimal human intervention, mimicking the structure and function of the human brain. This involves advancements in architectures like convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequential data, and transformers for natural language processing. DL research aims to improve model efficiency, scalability, and interpretability, enabling applications in areas such as computer vision, speech recognition, autonomous vehicles, and healthcare. Key challenges include reducing computational costs, enhancing generalization capabilities, and addressing ethical concerns like bias and privacy, driving ongoing innovation in algorithms, hardware, and data management.
