Table 1 The main differences between CNNs and RNNs
CNN | RNN | |
|---|---|---|
Uses | CNNs are commonly used to solve problems involving spatial data, such as images | RNNs are better suited to analyzing temporal and sequential data, such as text or videos |
Architectures | CNNs are feedforward neural networks that use filters and pooling layers | RNNs feed results back into the network |
size of the input and the resulting output | In CNNs, the size of the input and the resulting output are fixed. A CNN receives images of fixed size and outputs a predicted class label for each image along with a confidence level | In RNNs, the size of the input and the resulting output can vary |
Common use cases | Common use cases for CNNs include facial recognition, medical analysis and image classification | Common use cases for RNNs include machine translation, natural language processing, sentiment analysis and speech analysis |