问题
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?
回答1:
Difference between CNN and RNN are as follows:
CNN:
CNN takes a fixed size inputs and generates fixed-size outputs.
CNN is a type of feed-forward artificial neural network - are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing.
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.
CNNs are ideal for images and video processing.
RNN:
RNN can handle arbitrary input/output lengths.
RNN unlike feedforward neural networks - can use their internal memory to process arbitrary sequences of inputs.
Recurrent neural networks use time-series information. i.e. what I spoke last will impact what I will speak next.
RNNs are ideal for text and speech analysis.
回答2:
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: http://rnd.azoft.com/classification-eeg-signals-brain-computer-interface/
回答3:
Obviously I am a little late here, but I would like to point anyone who is interested in neural nets to this article. Not only does it explain the basics fairly well, but it also gives you the original papers if you want to dig deeper, while addressing all of the most common Neural Net architectures today.
ice.cube answered very well regarding principal uses of each
回答4:
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.
Convolutional networks are networks with overlapping "reception fields" performing convolution tasks.
Recurrent networks are networks with recurrent connections (going in the opposite direction of the "normal" signal flow) which form cycles in the network's topology.
回答5:
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.
回答6:
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.
回答7:
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.
回答8:
First, we need to know that recursive NN is different from recurrent NN. By wiki's definition,
A recursive neural network (RNN) is a kind of deep neural network created by applying the same set of weights recursively over a structure
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.
来源：https://stackoverflow.com/questions/20923574/whats-the-difference-between-convolutional-and-recurrent-neural-networks