How deeply does Graph Neural Networks in Action cover sequence prediction?

Builds an RNN-based training and testing workflow, including memory, preprocessing, optimization, and multi-step rollout.

Sequence prediction

As explained in Graph Neural Networks in Action

A prediction task in which a model uses ordered past observations to predict subsequent observations or events.

Sequence prediction uses ordered past observations to infer what happens next. Typical uses include autocomplete, search, and forecasting sensor positions or pose changes from a few input frames. section 6.2.2

A practical sequence-prediction workflow:

Prepare temporal inputs and labels from observed events or frames. section 6.2.2
Use memory so earlier observations influence later predictions; recurrent neural networks implement this through linked subnetworks. section 6.2.2

Sensor-position forecasting

Given several frames of sensor data, predict the next position for every sensor and continue producing future movements from the model's previous outputs.

How Graph Neural Networks in Action covers it

Cover of Graph Neural Networks in Action

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Also explained in

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Build a Text-to-Image Generator (from Scratch)A prediction task in which a model generates or estimates elements of an ordered sequence.
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Learn Generative AI with PyTorchA learning setup where a model predicts the next element in an ordered sequence from earlier elements.

Compare how 3 books explain sequence prediction →

Related concepts

text-to-image modelincludes · A text-to-image model is a generative machine-learning model that produces images conditioned on textual descriptions or prompts.
transformer architectureaddressed by · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
recurrent neural networkuses · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.
next-token predictionincludes · Next-token prediction is the task or training objective of estimating the token that follows a sequence, given its preceding tokens and any conditioning information.
sequence generationkind of · Sequence generation is the process of producing an ordered sequence of tokens, characters, or other elements, typically conditioned on input or prior elements.

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.