Deep Learning Framework

Deep learning frameworks are computational tools designed to build and train artificial neural networks for diverse applications. Current research emphasizes developing frameworks tailored to specific data types (e.g., tabular, temporal, image, audio) and tasks (e.g., classification, regression, anomaly detection), often incorporating architectures like convolutional neural networks, recurrent neural networks, transformers, and graph neural networks. These frameworks are significantly impacting various fields, from improving medical image analysis and accelerating scientific simulations to optimizing industrial processes and enhancing personalized advertising strategies.