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RGB

As explained in Inside AI

The RGB color system represents colors as combinations of red, green, and blue component values.

RGB represents image colors as combinations of red, green, and blue values. In deep learning, each pixel becomes three numbers, and an image becomes a vector of pixel values. section 3.4

Color photographs of cats and dogs

A cat-or-dog classifier receives photographs whose pixels are encoded as RGB triples. The network uses these numerical image representations during training, then evaluates predictions on separate, previously unseen test images. section 3.4

An RGB image pipeline uses the representation to:

  • Translate image colors into numbers ranging from 0 to 1. section 3.4
  • Describe every pixel with three values corresponding to red, green, and blue. section 3.4

How Inside AI covers it

How deep it goes
Defines pixel-level RGB encoding and image vectors, but stops short of implementation details.
Examples
Uses color photographs of cats and dogs, with each pixel represented by a red-green-blue value set.
What it assumes
Assumes basic familiarity with images and neural networks while explaining pixel values from the ground up.
What you can do after
Apply RGB by converting image colors into numerical pixel inputs for a classification model.
How it connects
Connects RGB with deep learning, neural networks, image classification, convolutional neural networks, and vision transformers.
Cover of Inside AI
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This explainer was generated by Manning's knowledge graph from the text of Inside AI. The section links point to the parts of the book it draws on.