What is the difference between Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)?

DNN: There is a problem - it is not possible to model variations in time series. However, the temporal order in which samples appear is very important for applications such as natural language processing, speech recognition, and handwriting recognition. To accommodate this need, another neural network structure, the recurrent neural network RNN, has emerged.

CNNs have excelled in large-scale image processing, and are now used on a large scale for image classification, localization, and other fields. Compared to other neural network structures, convolutional neural networks require relatively few parameters, allowing them to be widely used.

Broadly speaking, NNs (or more beautifully, DNNs) can indeed be thought of as encompassing specific variant forms of CNNs and RNNs. In practice, so-called deep neural networks, DNNs, often incorporate a variety of known structures, including convolutional layers or LSTM units.