How deeply does Machine Learning Algorithms in Depth cover Vision Transformer?

Defines patch embedding, positional embedding, transformer encoding, and classification but does not derive their internal operations.

Vision Transformer

As explained in Machine Learning Algorithms in Depth

A vision transformer is a transformer-based image model that processes an image as a sequence of embedded patches.

A Vision Transformer (ViT) applies a transformer-like architecture to images by converting an image into a sequence of embedded patches. Each fixed-size patch receives a linear embedding and a positional embedding before entering a transformer encoder. section 11.5

The image-classification pipeline consists of:

Divide the image into fixed-size patches. section 11.5
Linearly embed each patch and add positional information. section 11.5
Feed the resulting vector sequence to a standard transformer encoder. section 11.5
Pass the encoder output to an MLP head for classification. section 11.5

Image classification from patches

Given an image, represent every fixed-size region as a position-aware vector, process the ordered vectors with a transformer encoder, and use the resulting representation to classify the image. section 11.5

How Machine Learning Algorithms in Depth covers it

Cover of Machine Learning Algorithms in Depth

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Also explained in

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Deep Learning with PyTorch, Second EditionA transformer-based neural network adapted for computer vision tasks, especially image classification, by processing image patches with attention.
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AI-Powered SearchA vision transformer is a transformer-based neural model that represents and processes visual data such as images.
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Inside AIA vision transformer is a transformer-based model that processes image representations for computer-vision tasks.

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Related concepts

transformer architecturekind of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
scaled-dot product attentionuses · Scaled dot-product attention computes query-key dot products, divides them by the square root of the key dimension, applies softmax, and uses the resulting weights to combine value vectors.
patchuses · A patch is a localized segment or subregion of data treated as a unit for representation or processing.
convolutional neural networkalternative to · A convolutional neural network is a neural-network architecture that applies learned convolutional operations to extract patterns from structured data.
attentionuses · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning Algorithms in Depth. The section links point to the parts of the book it draws on.