1. Prototypical networks are simple, efficient, and one of the most popularly used few-shot learning algorithms. The basic idea of the prototypical network is to create a prototypical representation of each class and classify a query point (new point) based on the distance between the class prototype and the query point.
2. We compute embeddings for each of the data points to learn the features. 
3. Once we learn the embeddings of each data point, we take the mean embeddings of data points in each class and form the class prototype. So, a class prototype is basically the mean embeddings of data points in a class.
4. In a Gaussian prototypical network, along with generating embeddings for the data points, we add a confidence region around them, which is characterized by a Gaussian covariance matrix. Having a confidence region helps to characterize the quality of individual data points, and it is useful with noisy and less homogeneous data. 
5. Gaussian prototypical networks differ from vanilla prototypical networks in that in a vanilla prototypical network, we learn only the embeddings of a data point, but in a Gaussian prototypical network, along with learning embeddings, we also add a confidence region to them. 
6. The radius and diagonal are the different components of the covariance matrix used in a Gaussian prototypical network.