TensorFlow – CNN And RNN Difference
In this chapter, we will focus on the difference between CNN and RNN −
| CNN | RNN |
|---|---|
| It is suitable for spatial data such as images. | RNN is suitable for temporal data, also called sequential data. |
| CNN is considered to be more powerful than RNN. | RNN includes less feature compatibility when compared to CNN. |
| This network takes fixed size inputs and generates fixed size outputs. | RNN can handle arbitrary input/output lengths. |
| CNN is a type of feed-forward artificial neural network with variations of multilayer perceptrons designed to use minimal amounts of preprocessing. | RNN unlike feed forward neural networks – can use their internal memory to process arbitrary sequences of inputs. |
| CNNs use connectivity pattern between the neurons. This is inspired by the organization of the animal visual cortex, whose individual neurons are arranged in such a way that they respond to overlapping regions tiling the visual field. | Recurrent neural networks use time-series information – what a user spoke last will impact what he/she will speak next. |
| CNNs are ideal for images and video processing. | RNNs are ideal for text and speech analysis. |
Following illustration shows the schematic representation of CNN and RNN −
