How deeply does Deep Learning with PyTorch, Second Edition cover ResNet?

Defines identity mappings, gradient shortcuts, and PyTorch blocks, but stops short of detailed residual-network variants.

ResNet

As explained in Deep Learning with PyTorch, Second Edition

A family of residual neural network architectures that use skip connections to ease training of deep models.

ResNet is a deep neural-network architecture that adds a block’s input directly to its output through a skip connection. This shortcut helps deep models train by creating a more direct path for gradients, reducing problems caused by long chains of multiplications. section 8.5.3

A residual block in PyTorch

A residual block applies convolution, batch normalization, and ReLU, then returns `out + x`. Stacking these blocks creates a much deeper network; the example uses 100 blocks in `NetResDeep`.

How Deep Learning with PyTorch, Second Edition covers it

Cover of Deep Learning with PyTorch, Second Edition

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Also explained in

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Sutskever's ListResNet is a deep neural network architecture that uses residual connections to facilitate the training of very deep networks.
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Inside Deep Learning: Math, Algorithms, ModelsA neural network architecture built from residual blocks with skip connections that help information and gradients propagate through deep networks.
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Math and Architectures of Deep LearningA deep convolutional neural network architecture that uses identity shortcut connections to ease optimization of very deep networks.

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Related concepts

residual connectionuses · A residual connection is a neural-network pathway that carries an earlier representation around one or more transformations to a later computation, where it can be combined with the transformed representation.
vanishing gradient problemaddresses · The vanishing-gradient problem is a neural-network training difficulty in which gradients shrink toward zero as they propagate backward, making updates to earlier layers or distant sequence steps ineffective.
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.
Faster R-CNNused by · Faster R-CNN is an object-detection architecture that uses a learned region proposal network and detection heads to localize and classify objects in images.
ResNet50includes · ResNet50 is a 50-layer residual convolutional neural network architecture for visual recognition and image processing.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with PyTorch, Second Edition. The section links point to the parts of the book it draws on.