blocks|key|2560990|text|CNN与RNN的区别如下：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2560991|CNN：|2560992|CNN接受固定大小的输入并生成固定大小的输出。|ordered-list-item|2560993|CNN是一种前馈人工神经网络，是多层感知器的一种变体，其目的是利用最少的预处理量。|2560994|CNN使用其神经元之间的连接模式，并受到动物视觉皮层组织的启发，其单个神经元的排列方式使其对重叠的视觉区域作出反应。|2560995|CNN是理想的图像和视频处理。|2560996|RNN：|2560997|RNN可以处理任意输入/输出长度。|2560998|RNN与前馈神经网络不同，它可以利用内部记忆来处理任意的输入序列。|2560999|递归神经网络使用时间序列信息.也就是说，我最后说的话会影响我接下来要讲的内容。|2561000|RNNs是文本分析和语音分析的理想方法。|2561001|entityMap^0|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Z|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|10|8|@]|9|@]|A|$]]|$1|G|3|H|5|F|7|11|8|@]|9|@]|A|$]]|$1|I|3|J|5|F|7|12|8|@]|9|@]|A|$]]|$1|K|3|L|5|F|7|13|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|14|8|@]|9|@]|A|$]]|$1|O|3|P|5|F|7|15|8|@]|9|@]|A|$]]|$1|Q|3|R|5|F|7|16|8|@]|9|@]|A|$]]|$1|S|3|T|5|F|7|17|8|@]|9|@]|A|$]]|$1|U|3|V|5|F|7|18|8|@]|9|@]|A|$]]|$1|W|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|X|$]]

Difference between CNN and RNN are as follows:

<h1>CNN:</h1>

<ol>
<li>CNN takes a fixed size inputs and generates fixed-size outputs.</li>
<li>CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.</li>
<li>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.</li>
<li>CNNs are ideal for images and video processing.</li>
</ol>

<h1>RNN:</h1>

<ol>
<li>RNN can handle arbitrary input/output lengths.</li>
<li>RNN unlike feedforward neural networks - can use their internal memory to process arbitrary sequences of inputs.</li>
<li>Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.</li>
<li>RNNs are ideal for text and speech analysis.</li>
</ol>

blocks|key|2560948|text|卷积神经网络(CNN)是用来识别图像的。它的内部有卷积，可以看到图像上识别的物体的边缘。递归神经网络(RNN)被设计用于识别序列，例如语音信号或文本。递归网络内部有循环，这意味着网络中存在短内存。我们应用CNN和RNN选择合适的机器学习算法对脑电信号进行分类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2560949|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Convolutional neural networks (CNN) are designed to recognize images. It has convolutions inside, which see the edges of an object recognized on the image. Recurrent neural networks (RNN) are designed to recognize sequences, for example, a speech signal or a text. The recurrent network has cycles inside that implies the presence of short memory in the net. We have applied CNN as well as RNN choosing an appropriate machine learning algorithm to classify EEG signals for BCI: <a href="http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/" rel="noreferrer">http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/</a>

blocks|key|859924|text|这些架构是完全不同的，所以很难说“区别是什么”，因为唯一的共同点是，它们都是神经网络。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|859925|卷积网络是具有重叠的“接收域”来执行卷积任务的网络。|offset|length|859926|递归网络是具有递归连接的网络(与“正常”信号流相反的方向)，在网络拓扑中形成循环。|style|BOLD|859927|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Convolutional_neural_network|1|http://en.wikipedia.org/wiki/Recurrent_neural_network^0|0|0|4|0|0|12|2|0|4|1|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@$D|V|E|W|1|X]]|A|$]]|$1|F|3|G|5|6|7|Y|8|@$D|Z|E|10|H|I]]|9|@$D|11|E|12|1|13]]|A|$]]|$1|J|3|-4|5|6|7|14|8|@]|9|@]|A|$]]]|K|$L|$5|M|N|O|A|$P|Q]]|R|$5|M|N|O|A|$P|S]]]]

These architectures are completely different, so it is rather hard to say "what is the difference", as the only thing in common is the fact, that they are both neural networks.

<a href="http://en.wikipedia.org/wiki/Convolutional_neural_network" rel="nofollow noreferrer">Convolutional networks</a> are networks with overlapping "reception fields" performing convolution tasks. 

<a href="http://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow noreferrer">Recurrent networks</a> are networks with recurrent connections (going in the opposite direction of the "normal" signal flow) which form cycles in the network's topology.

blocks|key|389851|text|除了其他外，在CNN中，我们通常使用沿轴和卷积线的2d平方滑动窗口来识别模式。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|389852|在RNN中，我们使用先前计算过的内存。如果您感兴趣，您可以看到，LSTM+(长期短期记忆)，这是一种特殊的RNN。|389853|CNN和RNN都有一个共同点，因为它们检测模式和序列，即不能对单个输入数据位进行洗牌。|389854|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Apart from others, in CNN we generally use a 2d squared sliding window along an axis and convolute (with original input 2d image) to identify patterns. 

In RNN we use previously calculated memory. If you are interested you can see, LSTM (Long Short-Term Memory) which is a special kind of RNN.

Both CNN and RNN have one point in common, as they detect patterns and sequences, that is you can't shuffle your single input data bits.

blocks|key|858962|text|卷积神经网络(CNNs)用于计算机视觉，递归神经网络(RNNs)用于自然语言处理。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|858963|虽然这可以应用于其他领域，但RNN具有网络的优势，即通过在网络中引入环路，可以使信号在两个方向上移动。|858964|反馈网络功能强大，并且会变得非常复杂。从前一个输入导出的计算被反馈到网络中，这给了他们一种记忆。反馈网络是动态的:它们的状态不断变化，直到达到平衡点。|858965|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Convolutional neural networks (CNNs) for computer vision, and recurrent neural networks (RNNs) for natural language processing. 

Although this can be applied in other areas, RNNs have the advantage of networks that can have signals travelling in both directions by introducing loops in the network. 

Feedback networks are powerful and can get extremely complicated. Computations derived from the previous input are fed back into the network, which gives them a kind of memory. Feedback networks are dynamic: their state is changing continuously until they reach an equilibrium point.

blocks|key|389815|text|首先，我们需要知道递归神经网络和递归神经网络是不同的。根据维基的定义，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|389816|递归神经网络(RNN)是一种在结构上递归应用同一组权值的深层神经网络。|blockquote|389817|从这个意义上讲，CNN是一种递归神经网络。另一方面，递归神经网络是一种基于时差的递归神经网络。因此，在我看来，CNN和递归NN是不同的，但两者都来自递归NN。|389818|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$]]|$1|E|3|F|5|6|7|K|8|@]|9|@]|A|$]]|$1|G|3|-4|5|6|7|L|8|@]|9|@]|A|$]]]|H|$]]

First, we need to know that recursive NN is different from recurrent NN.
By wiki's definition,

<blockquote>
 A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure
</blockquote>

In this sense, CNN is a type of Recursive NN.
On the other hand, recurrent NN is a type of recursive NN based on time difference.
Therefore, in my opinion, CNN and recurrent NN are different but both are derived from recursive NN.

blocks|key|860020|text|这就是CNN和RNN之间的区别。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|860021|卷积神经NEtwork:|offset|length|style|BOLD|860022|在深度学习中，卷积神经网络(+ConvNet)是一类最常用于视觉图像分析的深层神经网络。..。它们在图像和视频识别、推荐系统、图像分类、医学图像分析和自然语言处理等方面都有应用。|860023|递归神经网络：|860024|递归神经网络(RNN)是一类人工神经网络，节点间的连接沿时间序列形成有向图。这使得它能够显示时间上的动态行为。与前馈神经网络不同，RNNs可以利用内部状态(内存)来处理输入序列。|860025|entityMap^0|0|0|C|0|0|0|7|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Q|8|@$D|R|E|S|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|T|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|U|8|@$D|V|E|W|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|X|8|@]|9|@]|A|$]]|$1|N|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|O|$]]

This is the difference between CNN and RNN

Convolutional Neural NEtwork:

In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. ... They have applications in image and video recognition, recommender systems, image classification, medical image analysis, and natural language processing.

Recurrent Neural Networks:

A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

blocks|key|2561191|text|首先对卷积层和递归层进行描述更有帮助。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2561192|卷积层：|offset|length|style|BOLD|2561193|包括输入、一个或多个过滤器(以及次采样)。|2561194|输入可以是一维或n维(n>1)，例如，它可以是二维图像.在每个层中也定义了一个或多个过滤器。输入与每一个滤波器相转换。在图像处理中，卷积方法与滤波器的卷积方法几乎是相似的。通常，本节的目的是从输入中提取每个过滤器的特性。每个卷积的输出称为特征映射。|2561195|例如，对水平边缘考虑滤波器，其与输入卷积的结果是提取输入图像的水平边缘。通常，在实践中，特别是在第一层中，定义了大量的过滤器(例如，一层中的60个过滤器)。另外，在卷积后，通常执行次采样操作，例如，选择两个邻域值​​中的每个值的最大值或平均值。|2561196|卷积层允许从输入中提取重要的特征和模式。并删除输入数据依赖项(线性和非线性)。|2561197|下图显示了使用卷积层和模式提取进行分类的示例。|2561198|1：https://i.stack.imgur.com/HS4U0.png+Kalhor，A.+(2020年)。NNs分类与回归讲课。|2561199|卷积层的优势：|2561200|能够消除相关性并减少输入维数|unordered-list-item|2561201|网络泛化在增加。|2561202|网络鲁棒性增强，因为它提取了关键特征。|2561203|非常强大，在监督学习中被广泛应用。|2561204|..。|2561205|递归层：|2561206|在这些层中，当前层的输出或下一层的输出也可以用作该层的输入。它还可以接收时间序列作为输入。|2561207|不使用递归层的输出如下(一个简单的例子)：|2561208|y+=+f(W+*+x)|code-block|syntax|javascript|2561209|其中x是输入，W是权，f是激活函数。|2561210|但在经常性网络中，情况如下：|2561211|y+=+f(W+*+x)
y+=+f(W+*+y)
y+=+f(W+*+y)
...+until+convergence|2561212|这意味着在这些网络中，生成的输出可以用作输入，从而具有存储网络。一类递归网络是离散Hopfield网和递归自联想网，它们是简单的网络或复杂的网络，如LSTM。|2561213|下面的图像显示了一个示例。|2561214|递归层的优势：|2561215|他们有记忆能力|2561216|它们可以使用时间序列作为输入。|2561217|它们可以使用生成的输出供以后使用。|2561218|非常适用于机器翻译，语音识别，图像描述。|2561219|2561220|使用卷积层的网络称为卷积网络(CNN)。类似地，使用递归层的网络被称为递归网络。还可以根据所需的应用程序在网络中使用两个层！|2561221|entityMap|0|LINK|mutability|MUTABLE|url|https://i.stack.imgur.com/HS4U0.png|1|https://i.stack.imgur.com/1xoKv.png^0|0|0|4|0|0|2H|9|0|0|0|13|0|0|2|Z|0|0|4|3|0|0|0|0|0|0|0|4|0|0|0|0|0|0|0|0|0|D|1|0|4|3|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|2E|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|2F|8|@$D|2G|E|2H|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|2I|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|2J|8|@$D|2K|E|2L|F|G]]|9|@]|A|$]]|$1|L|3|M|5|6|7|2M|8|@]|9|@]|A|$]]|$1|N|3|O|5|6|7|2N|8|@$D|2O|E|2P|F|G]]|9|@]|A|$]]|$1|P|3|Q|5|6|7|2Q|8|@]|9|@]|A|$]]|$1|R|3|S|5|6|7|2R|8|@]|9|@$D|2S|E|2T|1|2U]]|A|$]]|$1|T|3|U|5|6|7|2V|8|@$D|2W|E|2X|F|G]]|9|@]|A|$]]|$1|V|3|W|5|X|7|2Y|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|X|7|2Z|8|@]|9|@]|A|$]]|$1|10|3|11|5|X|7|30|8|@]|9|@]|A|$]]|$1|12|3|13|5|X|7|31|8|@]|9|@]|A|$]]|$1|14|3|15|5|X|7|32|8|@]|9|@]|A|$]]|$1|16|3|17|5|6|7|33|8|@$D|34|E|35|F|G]]|9|@]|A|$]]|$1|18|3|19|5|6|7|36|8|@]|9|@]|A|$]]|$1|1A|3|1B|5|6|7|37|8|@]|9|@]|A|$]]|$1|1C|3|1D|5|1E|7|38|8|@]|9|@]|A|$1F|1G]]|$1|1H|3|1I|5|6|7|39|8|@]|9|@]|A|$]]|$1|1J|3|1K|5|6|7|3A|8|@]|9|@]|A|$]]|$1|1L|3|1M|5|1E|7|3B|8|@]|9|@]|A|$1F|1G]]|$1|1N|3|1O|5|6|7|3C|8|@]|9|@]|A|$]]|$1|1P|3|1Q|5|6|7|3D|8|@]|9|@$D|3E|E|3F|1|3G]]|A|$]]|$1|1R|3|1S|5|6|7|3H|8|@$D|3I|E|3J|F|G]]|9|@]|A|$]]|$1|1T|3|1U|5|X|7|3K|8|@]|9|@]|A|$]]|$1|1V|3|1W|5|X|7|3L|8|@]|9|@]|A|$]]|$1|1X|3|1Y|5|X|7|3M|8|@]|9|@]|A|$]]|$1|1Z|3|20|5|X|7|3N|8|@]|9|@]|A|$]]|$1|21|3|15|5|X|7|3O|8|@]|9|@]|A|$]]|$1|22|3|23|5|6|7|3P|8|@]|9|@]|A|$]]|$1|24|3|-4|5|6|7|3Q|8|@]|9|@]|A|$]]]|25|$26|$5|27|28|29|A|$2A|2B]]|2C|$5|27|28|29|A|$2A|2D]]]]

It is more helpful to describe the convolution and recurrent layers first.
Convolution layer:
Includes input, one or more filters (as well as subsampling).
The input can be one-dimensional or n-dimensional (n&gt;1), for example, it can be a two-dimensional image. One or more filters are also defined in each layer. Inputs are convolving with each filter. The method of convolution is almost similar to the convolution of filters in image processing. In general, the purpose of this section is to extract the features of each filter from the input. The output of each convolution is called a feature map.
For example, a filter is considered for horizontal edges, and the result of its convolution with the input is the extraction of the horizontal edges of the input image. Usually, in practice and especially in the first layers, a large number of filters (for example, 60 filters in one layer) are defined. Also, after convolution, the subsampling operation is usually performed, for example, their maximum or average of each of the two neighborhood values ​​is selected.
The convolution layer allows important features and patterns to be extracted from the input. And delete input data dependencies (linear and nonlinear).
[The following figure shows an example of the use of convolutional layers and pattern extraction for classification.][1]
[1]: <a href="https://i.stack.imgur.com/HS4U0.png" rel="nofollow noreferrer">https://i.stack.imgur.com/HS4U0.png</a> [Kalhor, A. (2020). Classification and Regression NNs. Lecture.]
Advantages of convolutional layers:
<ul>
<li>Able to remove correlations and reduce input dimensions
</li>
<li>Network generalization is increasing
</li>
<li>Network robustness increases against changes because it extracts key features
</li>
<li>Very powerful and widely used in supervised learning
</li>
<li>...
</li>
</ul>
Recurrent layers:
In these layers, the output of the current layer or the output of the next layers can also be used as the input of the layer. It also can receive time series as input.
The output without using the recurrent layer is as follows (a simple example):
<pre><code>y = f(W * x)
</code></pre>
Where x is input, W is weight and f is the activator function.
But in recurrent networks it can be as follows:
<pre><code>y = f(W * x)
y = f(W * y)
y = f(W * y)
... until convergence
</code></pre>
This means that in these networks the generated output can be used as an input and thus have memory networks. Some types of recurrent networks are Discrete Hopfield Net and Recurrent Auto-Associative NET, which are simple networks or complex networks such as LSTM.
<a href="https://i.stack.imgur.com/1xoKv.png" rel="nofollow noreferrer">An example is shown in the image below.</a>
Advantages of Recurrent Layers:
<ul>
<li>They have memory capability
</li>
<li>They can use time series as input.
</li>
<li>They can use the generated output for later use.
</li>
<li>Very used in machine translation, voice recognition, image description
</li>
<li>...
</li>
</ul>
Networks that use convolutional layers are called convolutional networks (CNN). Similarly, networks that use recurrent layers are called recurrent networks. It is also possible to use both layers in a network according to the desired application!

I'm new to the topic of neural networks. I came across the two terms convolutional neural network and recurrent neural network. 

I'm wondering if these two terms are referring to the same thing, or, if not, what would be the difference between them?

What's the difference between convolutional and recurrent neural networks?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我对神经网络这个话题很陌生。我遇到了两个术语--卷积神经网络和递归神经网络。我想知道这两个术语是否是指同一事物，或者，如果不是，它们之间会有什么区别呢？

问卷积神经网络和递归神经网络有什么区别？
EN

回答 8

Stack Overflow用户

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问卷积神经网络和递归神经网络有什么区别？EN

回答 8

Stack Overflow用户

Stack Overflow用户

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腾讯云开发者

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问卷积神经网络和递归神经网络有什么区别？
EN