Probabilistic Deep Learning

Probabilistic Deep Learning is a technology that combines probabilistic modeling with deep learning, aiming to handle uncertainty through a probabilistic framework and optimize the generalization and robustness of models. Its core objective is to build deep neural networks capable of effective inference and prediction, thereby providing more reliable probabilistic estimates in data-driven decision-making processes. This technology has broad application value in fields such as natural language processing, computer vision, and reinforcement learning, especially in scenarios that require dealing with complex, uncertain, and high-dimensional data.