Hybrid Deep Learning

Hybrid deep learning combines the strengths of different deep learning architectures (e.g., CNNs, RNNs, Transformers) and sometimes traditional statistical models (e.g., GARCH) to improve performance on various tasks. Current research focuses on applying these hybrid models to diverse fields, including medical image analysis (segmentation, diagnosis), time series prediction (financial markets, internet traffic, water quality), and natural language processing (sentiment analysis, text generation detection). This approach leads to more accurate and robust models than using single architectures alone, impacting fields ranging from healthcare and finance to environmental monitoring and transportation planning.