How deeply does Math and Architectures of Deep Learning cover VGG?

Explains filter equivalence, nonlinearities, block structure, and PyTorch construction, but defers detailed training methodology.

VGG

As explained in Math and Architectures of Deep Learning

A family of convolutional neural networks characterized by repeated 3×3 convolutions and deep stacked blocks for image classification.

VGG is a family of deep convolutional neural networks for image classification. Its central design replaces large early kernels with repeated 3 × 3 convolutions, ReLU activations, and stacked convolutional blocks. Three 3 × 3 filters preserve the receptive field of one 7 × 7 filter while using 27C² rather than 49C² parameters and adding more nonlinearities. section 11.2.1 section 11.5

A VGG configuration typically follows these structural choices:

Process 224 × 224 images through five convolutional blocks. section 11.2.1
Increase depth across VGG-11, VGG-13, VGG-16, and VGG-19 configurations. section 11.2.1
Omit local response normalization because it did not improve accuracy. section 11.2.1

A VGG-11 configuration

Represent the convolutional backbone as [(3, 1, 64), (64, 1, 128), (128, 2, 256), (256, 2, 512), (512, 2, 512)]. Flatten its final features and pass them through fully connected layers of 4096, 4096, and num_classes outputs. In PyTorch, torchvision.models.vgg11() supplies a direct instantiation.

How Math and Architectures of Deep Learning covers it

Cover of Math and Architectures of Deep Learning

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Related concepts

linear layeruses · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
vision modelkind of · A vision model is a machine-learning model that processes visual data, such as images or video, to analyze, generate, or extract information.
foundational modelused by · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.
poolinguses · Pooling is an operation that aggregates values or representations across regions or axes to produce a reduced or summarized representation.
convolutional blockhas part · A repeated network unit composed of one or more convolution layers and a pooling step in the VGG architecture.

This explainer was generated by Manning's knowledge graph from the text of Math and Architectures of Deep Learning. The section links point to the parts of the book it draws on.