Discover how probability theory enhances deep learning models, enabling them to express uncertainty and make more reliable predictions. Learn techniques for building probabilistic neural networks that can accurately quantify confidence levels, handle noisy data, and make well-calibrated decisions. Master Bayesian approaches to deep learning that combine the power of modern neural networks with principled probabilistic modeling, leading to more robust and interpretable AI systems.