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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 while RNN is used for ideal for text and speech analysis.

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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 below). For example, in the image, the connection between pixels in some area gives you another feature (e.g. edge) instead of a feature from one pixel (e.g. color). So, as long as you can shaping your data ...

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The paper you are citing is the paper that introduced the cascaded convolution neural network. In fact, in this paper, the authors say To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the introduction of a new input feature which fulfills the "cascade manner" and ...

CBS News: Miami leads the nation in immigration arrests without the high-profile ICE raids, new data shows

An analysis by The New York Times found Florida is quietly outpacing the rest of the country, with the Miami-based field office averaging about 120 arrests a day.

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Miami leads the nation in immigration arrests without the high-profile ICE raids, new data shows

mlive: As ICE arrests keep climbing in Michigan, most are not criminals