<p>Both CNN and RNN fall into the super set of neural networks,however applications of the two matters.</p>

<p>So to branch them off in terms of applications,</p>

<blockquote>
  <p>I would say CNN’s are mainly used for vision related applications,
  whereas, RNN’s are mainly used for language processing applications.</p>
</blockquote>

<p>You can refer to these links for further details.</p>

<p><a href="https://arxiv.org/abs/1702.01923" rel="nofollow noreferrer">Comparative Study of CNN and RNN for Natural Language Processing
</a></p>

<p><a href="http://karpathy.github.io/2015/05/21/rnn-effectiveness/" rel="nofollow noreferrer">How are recurrent neural networks different from convolutional neural networks?</a>
<em>The unreasonable effectiveness of Recurrent Neural Networks</em></p>

<p>Hope this can give you a glimpse!</p>

CC BY-SA 3.0
2018-03-16T18:54:33.520Z
2018-03-16T18:54:33.520Z
quintumnia
<p>You are right. I think you are just misinterpreting the part of the sentence ('specifically LSTMs'). LSTMs are an example of a popular type of RNN.
RNNs and CNNs are different architectures but they can be used together.</p>

<p>Here is another sentence with the same structure:</p>

<blockquote>
  <p>It is clear than dogs, specifically corgis, and cats are very common in online memes.</p>
</blockquote>

<p><a href="https://i.sstatic.net/Ztt5e.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Ztt5e.jpg" alt="A cat and a corgi"></a></p>

CC BY-SA 3.0
2018-03-15T12:21:44.953Z
2018-03-15T13:04:38.150Z
2018-03-15T13:04:38.150Z
Jaden Travnik
<p>I asked my self this simple question while reading <a href="https://web.stanford.edu/class/cs224n/reports/2762092.pdf" rel="nofollow noreferrer">"Comment Abuse Classification with Deep Learning"</a> by Chu and Jue. Indeed, they say at the end of the that </p>

<blockquote>
  <p><em>It is clear that RNNs, specifically LSTMs, and CNNs are state-of-the-art  architectures for sentiment analysis</em></p>
</blockquote>

<p>To my mind CNNs were only neurons arranged so that they correspond to overlapping regions when paving the input field. It wasn't that recurrent at all.</p>

<convolutional-neural-networks><recurrent-neural-networks><comparison>
Do convolutional neural networks also have recurrent connections?
Revolucion for Monica
# Do convolutional neural networks also have recurrent connections?

(Asked by: username_0 on 2018-03-15)

I asked my self this simple question while reading ["Comment Abuse Classification with Deep Learning"](https://web.stanford.edu/class/cs224n/reports/2762092.pdf) by Chu and Jue. Indeed, they say at the end of the that

> *It is clear that RNNs, specifically LSTMs, and CNNs are state-of-the-art architectures for sentiment analysis*

To my mind CNNs were only neurons arranged so that they correspond to overlapping regions when paving the input field. It wasn't that recurrent at all.

**Tags:** convolutional-neural-networks, recurrent-neural-networks, comparison


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## 2 answers

### Answer by username_2 on 2018-03-15. Score: 4 | Accepted answer

You are right. I think you are just misinterpreting the part of the sentence ('specifically LSTMs'). LSTMs are an example of a popular type of RNN.
RNNs and CNNs are different architectures but they can be used together.

Here is another sentence with the same structure:

> It is clear than dogs, specifically corgis, and cats are very common in online memes.

[![A cat and a corgi](https://i.sstatic.net/Ztt5e.jpg)](https://i.sstatic.net/Ztt5e.jpg)


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### Answer by username_1 on 2018-03-16. Score: 3

Both CNN and RNN fall into the super set of neural networks,however applications of the two matters.

So to branch them off in terms of applications,

> I would say CNN’s are mainly used for vision related applications,
> whereas, RNN’s are mainly used for language processing applications.

You can refer to these links for further details.

[Comparative Study of CNN and RNN for Natural Language Processing](https://arxiv.org/abs/1702.01923)

[How are recurrent neural networks different from convolutional neural networks?](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
*The unreasonable effectiveness of Recurrent Neural Networks*

Hope this can give you a glimpse!


---

