Neural Processes are a class of machine learning models designed to learn from data in a way that mimics human cognitive functions. They focus on understanding and predicting complex patterns by leveraging the relationships between inputs and outputs. These models are particularly useful in scenarios where data is scarce or when quick adaptations to new tasks are required. One key feature of Neural Processes is their ability to generate distributions over functions, allowing them to provide uncertainty estimates in their predictions. This makes them valuable in various applications, including robotics, computer vision, and natural language processing, where understanding variability is crucial for decision-making.