Forward pass
A forward pass is a calculation that propagates inputs through an ordered computational or dependency structure to produce outputs or derived values.
Statistics Every Programmer Needs
A forward pass is a project-scheduling calculation that determines each activity's earliest start and earliest finish times by progressing through its dependencies.
- How it is used
- Schedules activities as early as dependencies permit to establish the project timeline and support critical path analysis.
- How deep it goes
- Defines ES and EF, explains predecessor handling, demonstrates a network, and implements the calculation in Python.
- Examples
- Uses an automated reporting tool network, activities A and B, a 29-week timeline, and a tabular CPM output.
- What you can do after
- Build it from scratch with Python, pandas, and NumPy to calculate earliest schedule times for dependent activities.
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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 it is used
- Produces neural-network outputs during supervised training before loss calculation and parameter adjustment.
- 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 you can do after
- Apply it via NumPy to compute logits, class probabilities, and loss from neural-network parameters.
Deep Learning with R, Third Edition
A forward pass is the propagation of input values through a computational graph in the direction of its outputs.
- How it is used
- Uses input batches to produce predictions before loss calculation and parameter updates during neural-network training.
- How deep it goes
- Defines graph-wide value propagation and illustrates it with scalar arithmetic, stopping before detailed implementation of large-network execution.
- Examples
- Uses scalar multiplication and addition, a two-layer model, and a mini-batch training loop to demonstrate prediction.
- What you can do after
- Understand how neural networks turn input batches into predictions and how those predictions supply loss values for optimization.
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Natural Language Processing in Action, Second Edition
Forward propagation is the computation of a neural network's output by passing inputs through its units in the forward direction.
- How it is used
- Computes predictions before loss evaluation, backpropagation, and optimizer updates.
- How deep it goes
- Explains single-neuron and layered propagation, then demonstrates a PyTorch implementation without developing forward-pass mathematics further.
- Examples
- Uses a basic perceptron, a layered network, and PyTorch name-gender predictions from TF-IDF features.
- What you can do after
- Apply it via PyTorch by defining forward(), generating predictions, and feeding them into a loss function.
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Grokking AI Algorithms, Second Edition
A forward pass is the computation that propagates input through a model to produce predictions.
- How it is used
- Produces predictions from inputs during neural-network inference and supplies predictions for loss calculation during training.
- How deep it goes
- Traces weighted sums, hidden and output activations, matrix operations, and a compact equation, while deferring weight adjustment to backpropagation.
- Examples
- Uses a scaled car-collision dataset, pretrained connection weights, sigmoid calculations, Python code, and Transformer token batches.
- What you can do after
- Apply it to trace or implement prediction flow through a multilayer neural network before calculating loss or updating weights.
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Math and Architectures of Deep Learning
The layer-by-layer computation, or forward pass, that produces neural-network activations and outputs from an input.
- How it is used
- Uses layer-by-layer inference to generate neural-network predictions and feed supervised-training loss.
- How deep it goes
- Derives affine and activation computations, explains sequential evaluation, and demonstrates a PyTorch loop without expanding the full nested function.
- Examples
- Uses layered MLP equations, a PyTorch forward function, and a two-layer height-and-weight classifier.
- What you can do after
- Apply it via PyTorch to compute model scores, predictions, and training losses.
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Deep Learning with JAX
The computation that applies a neural network to input data to produce output activations or predictions.
- How it is used
- Uses a pure function to apply a JAX neural network to input data and produce predictions.
- 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 you can do after
- Build it from scratch by writing a parameterized JAX prediction function and inspecting its output shape.
Deep Learning with PyTorch, Second Edition
The stage of model execution that propagates inputs through the network to produce outputs and loss values.
- How it is used
- Uses the forward pass to turn inputs into predictions and losses during model training.
- How deep it goes
- Explains its place in training and multi-GPU execution, but does not detail layer-level computations.
- Examples
- Demonstrates noisy-data parameter estimation and independent processing of batch shards across two GPUs.
- What you can do after
- Apply it via PyTorch as the first step of a training loop before loss calculation and backpropagation.
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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.
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