multi-head self-attention进行特征学习，和CNN，RNN进行特征学习的区别

Multi-head self-attention is a method used in Transformer models for natural language processing. It allows the model to focus on different parts of the input sequence when processing each token. CNN and RNN also perform feature learning, but they have different architectures and focus on different aspects of the input data. CNN is commonly used for image processing and learns features by scanning the image with a set of filters. RNN is commonly used for sequential data and learns features by propagating information through time. The choice of which method to use depends on the nature of the input data and the modeling task.