Forward pass

As explained in Deep Learning with JAX

The computation that applies a neural network to input data to produce output activations or predictions.

How Deep Learning with JAX covers it

How deep it goes

Explains layer operations, activation functions, parameter passing, and logits, while deferring training mechanics to gradient transformations.

Examples

Demonstrates a 784-input, two-layer network that returns ten logits for a flattened image.

What it assumes

Assumes basic neural-network layers and parameters while teaching JAX's explicit parameter passing from the computation itself.

What you can do after

Build it from scratch by writing a parameterized JAX prediction function and inspecting its output shape.

How it connects

Connects forward passes with activation functions, model parameters, functional purity, logits, and automatic differentiation.

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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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  Optimization Algorithms: AI techniques for design, planning, and control problemsForward propagation is the computation that passes inputs through successive neural-network layers to produce an output.
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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 PyTorch, Second EditionThe stage of model execution that propagates inputs through the network to produce outputs and loss values.

Compare how 8 books explain forward pass →

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 Deep Learning with JAX. The section links point to the parts of the book it draws on.