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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?
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The Convolutional Recurrent Neural Networks is the combination of two of the most prominent neural networks. The CRNN (convolutional recurrent neural network) involves CNN(convolutional neural network) followed by the RNN(Recurrent neural networks).
This article focuses on three important types of neural networks that form the basis for most pre-trained models in deep learning: Artificial Neural Networks (ANN) Convolution Neural Networks (CNN) Recurrent Neural Networks (RNN)
Why is CNN faster than RNN? CNNs are faster than RNNs because they are designed to handle images, while RNNs are designed to handle text. While RNNs can be trained to handle images, it's still difficult for them to separate contrasting features that are closer together.
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.
Difference between CNN and RNN are as follows:
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 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.
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