Convolutional Neural Networks, or CNNs, were designed to map image data to an output variable.
They have proven so effective that they are the go-to method for any type of prediction problem involving image data as an input. The benefit of using CNNs is their ability to develop an internal representation of a two-dimensional image. This allows the model to learn position and scale in variant structures in the data, which is important when working with images. Used for
- Image data
- Classification prediction problems
- Regression prediction problems
Convolutional Neural Networks, or CNNs, were designed to map image data to an output variable.They have proven so effective that they are the go-to method for any type of prediction problem involving image data as an input. The benefit of using CNNs is their ability to develop an internal representation of a two-dimensional image. This allows the model to learn position and scale in variant structures in the data, which is important when working with images. Used for
- Image data
- Classification prediction problems
- Regression prediction problems
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