google-deepmind/neural-processes resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1023 stars · 154 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebook implementations of the Neural Process family from DeepMind: Conditional Neural Processes (CNPs), Neural Processes (NPs), and Attentive Neural Processes (ANPs). The notebooks explain the model building blocks and can be run in the browser via Colab or locally with Jupyter and TensorFlow.

Use cases

• learn how conditional neural processes work
• implement neural processes in tensorflow
• understand attentive neural processes
• run neural process demos in colab
• study meta-learning regression models
• reproduce results from the CNP NP and ANP papers

When to choose

• you want readable, educational notebook implementations of the neural process family
• you want to run the models quickly without setup using Colab
• you are studying the original CNP, NP, and ANP papers and want reference code

When to avoid

• you need a production-ready or maintained neural process library
• you need modern TensorFlow 2 or PyTorch support
• you need features beyond the three original variants

Facets

learning-resource · maturity maintenance

machine-learning deep-learning data-visualization machine-learning deep-learning tutorials python cross-platform neural-processes jupyter-notebooks tensorflow regression probabilistic-models colab web-server

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markdown · JSON · MCP: product_card(name="google-deepmind/neural-processes")