Difference between CNN and RNN are as follows:
CNN:
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CNN takes a fixed size inputs and generates fixed-size outputs.
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CNN is a type of feed-forward artificial neural network – are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.
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CNNs use connectivity pattern between its neurons and 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.
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CNNs are ideal for images and video processing.
RNN:
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RNN can handle arbitrary input/output lengths.
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RNN unlike feedforward neural networks – can use their internal memory to process arbitrary sequences of inputs.
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Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.
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RNNs are ideal for text and speech analysis.