Читать книгу Computational Statistics in Data Science - Группа авторов - Страница 84

The previously introduced models have the same assumptions on the data, that is, the independence among the samples and the fixed input size. However, these assumptions may not be true in some cases, thus limiting the application of these models. For example, videos can have different lengths, and frames of the same video are not independent, and sentences of an chapter can have different lengths and are not independent.

A RNN is another modified DNN that is used primarily to handle sequential and time series data. In a RNN, the hidden layer of each input is a function of not just the input layer but also the previous hidden layers of the inputs before it. Therefore, it addresses the issues of dependence among samples and does not have any restriction on the input size. RNNs are used primarily in natural language processing applications, such as document modeling and speech recognition.