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

Explains module structure, Sequential execution, and model invocation, but does not descend into forward's internal autograd mechanics.

Forward

As explained in Math and Architectures of Deep Learning

The PyTorch module method that defines how inputs are transformed into outputs during a forward pass.

In PyTorch, forward defines how a module transforms inputs into outputs. A model subclasses torch.nn.Module, organizes layers in __init__, and implements forward to pass input data through those layers. section 8.5 section 10.6.1

Calling a model such as nn(X) invokes forward automatically. With torch.nn.Sequential, the input moves through each linear, activation, or convolutional layer in order, producing the prediction or feature output. section 8.5 section 10.6.1

Regression model

A two-layer demand-prediction network defines forward as `return self.model(X)`. Its Sequential model applies two Linear layers with Sigmoid activations and then a final Linear layer; training calls `y_out = nn(X)` before calculating MSE loss. section 8.5

Convolutional model

A sample CNN defines `forward(self, x)` by passing `x` to `self.nn`, a Sequential container of Conv2d layers and intervening layers, then returning the result. section 10.6.1

How Math and Architectures of Deep Learning covers it

Cover of Math and Architectures of Deep Learning

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Also explained in

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Deep Learning with PyTorch, Second EditionThe standard module-computation entry point in PyTorch, invoked by __call__ and used for the main forward pass.

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Related concepts

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torch.nn.Moduleimplemented by · torch.nn.Module is PyTorch’s base class for neural-network components that encapsulate computation, parameters, and submodules.
classpart of · A class is a programming-language construct that defines a type’s structure or behavior and provides a basis for creating or representing objects.
scikit-learnequivalent to · scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
functional APIuses · A functional API is an interface that exposes operations as callable functions, with operation inputs supplied through function arguments rather than method-owned state.
callbackused by · A callback is a function or other callable supplied to software for invocation later in response to a specified event, condition, or point in execution.

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.