ikatsov/tensor-house resource

Health v2 · maintenance only

Adoption not part of the score

1452 stars · 511 forks observed · 2026-08-28

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

TensorHouse is a collection of reference Jupyter notebooks and demo AI/ML applications covering enterprise use cases such as marketing, pricing, supply chain, and smart manufacturing. It includes readiness assessment questionnaires, datasets, data generators, and simulators to accelerate prototyping and evaluation of modeling approaches.

Use cases

• learn how to apply reinforcement learning to pricing optimization
• prototype supply chain optimization models
• explore enterprise marketing analytics notebooks
• evaluate readiness for an enterprise AI/ML project
• find sample datasets and simulators for model evaluation
• build a demo LLM application for a business use case
• study causal inference examples in Python

When to choose

• you need reference implementations of enterprise AI/ML use cases
• you want to prototype pricing, supply chain, or marketing models quickly
• you need questionnaires and templates to assess project readiness
• you want curated notebooks using TensorFlow, RLlib, DoWhy, or LangChain

When to avoid

• you need production-ready, deployable software rather than notebooks and prototypes
• your use case falls outside the covered enterprise domains
• you require a supported library with a stable API instead of example code

Facets

learning-resource · maturity active

machine-learning deep-learning reinforcement-learning data-science llm-inference simulation data-generation data-science machine-learning artificial-intelligence education tutorials python jvm jupyter-notebooks enterprise-ai reference-implementations pricing-optimization supply-chain marketing-analytics prototyping causal-inference sample-datasets

Member repositories

For agents

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