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What is the fundamental difference between CNN and RNN?

A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems. CNNs have become the go-to method for solving any image data challenge

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In a CNN (such as Google''s Inception network), bottleneck layers are added to reduce the number of feature maps (aka channels) in the network, which, otherwise, tend to increase in each layer. This is

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Extract features with CNN and pass as sequence to RNN

But if you have separate CNN to extract features, you can extract features for last 5 frames and then pass these features to RNN. And then you do CNN part for 6th frame and you pass

machine learning

The concept of CNN itself is that you want to learn features from the spatial domain of the image which is XY dimension. So, you cannot change dimensions like you mentioned.

What is the difference between a convolutional neural network and a

A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.

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What is the difference between CNN-LSTM and RNN?

Why would "CNN-LSTM" be another name for RNN, when it doesn''t even have RNN in it? Can you clarify this? What is your knowledge of RNNs and CNNs? Do you know what an LSTM is?

machine learning

Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an FCN is a CNN

convolutional neural networks

Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel. There are input_channels * number_of_filters sets of

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How to use CNN for making predictions on non-image data?

You can use CNN on any data, but it''s recommended to use CNN only on data that have spatial features (It might still work on data that doesn''t have spatial features, see DuttaA''s comment

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