Forward pass

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

Forward propagation is the computation that passes inputs through successive neural-network layers to produce an output.

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes

Explains layer-by-layer computation through activations, logits, probabilities, and loss, but does not detail gradient derivation.

Examples

Uses a NumPy implementation for three-class penguin classification with four input features and ten hidden units.

What it assumes

Assumes basic neural-network terminology while establishing the sequence from inputs and parameters to outputs and loss.

What you can do after

Apply it via NumPy to compute logits, class probabilities, and loss from neural-network parameters.

How it connects

Connects neural networks with weights, biases, activation functions, logits, loss functions, backpropagation, and particle swarm optimization.

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Also explained in

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  Statistics Every Programmer NeedsA forward pass is a project-scheduling calculation that determines each activity's earliest start and earliest finish times by progressing through its dependencies.
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  Deep Learning with R, Third EditionA forward pass is the propagation of input values through a computational graph in the direction of its outputs.
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  Natural Language Processing in Action, Second EditionForward propagation is the computation of a neural network's output by passing inputs through its units in the forward direction.
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  Grokking AI Algorithms, Second EditionA forward pass is the computation that propagates input through a model to produce predictions.
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  Math and Architectures of Deep LearningThe layer-by-layer computation, or forward pass, that produces neural-network activations and outputs from an input.
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  Deep Learning with JAXThe computation that applies a neural network to input data to produce output activations or predictions.
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  Deep Learning with PyTorch, Second EditionThe stage of model execution that propagates inputs through the network to produce outputs and loss values.

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Related concepts

• neural network layeruses · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• predicted valueproduces · A predicted value is an estimated outcome or output that a model produces for given input values.
• backpropagationcontrasts with · Backpropagation is a procedure for computing derivatives of an output or loss with respect to inputs and parameters by applying the chain rule backward through a computational graph.
• generationproduces · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.