# ikatsov/tensor-house

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Repository: https://github.com/ikatsov/tensor-house
Canonical: https://ross.abutalabs.com/products/tensor-house
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: data-science, ai, models, marketing, supply-chain, machine-learning, reinforcement-learning, deep-learning, customer-analysis, llm, personalization
Last push: 2024-01-24T15:36:36+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3204, "days_push": 952, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1452, forks 511 (observed 2026-08-28T04:04:46.137251+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, reinforcement-learning, data-science, llm-inference, simulation, data-generation
- domain: data-science, machine-learning, artificial-intelligence, education, tutorials
- platform: python, jvm
- tags: jupyter-notebooks, enterprise-ai, reference-implementations, pricing-optimization, supply-chain, marketing-analytics, prototyping, causal-inference, sample-datasets

## Member repositories
- ikatsov/tensor-house (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:46.137251+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:35:48.401094+00:00, confidence not recorded.
  - readme: https://github.com/ikatsov/tensor-house (fetched 2026-08-28T04:04:46.137251+00:00, sha be0617c4ed58)
- Data as of 2026-08-30T08:39:29.467469+00:00.