TensorHouse is a collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more. The goal of the project is to provide a toolkit for rapid readiness assessment, exploratory data analysis, and prototyping of various modeling approaches for typical enterprise AI/ML/data science projects.

TensorHouse contains the following resources:

• a well-documented repository of reference notebooks and demo applications (prototypes),
• a collection of readiness assessment and requirement gathering questionnaires for typical enterprise AI/ML projects,
• a manually curated list of important papers in enterprise AI,
• a manually curated list of public datasets related to enterprise use cases.

The project focuses on models, techniques, and datasets that were originally developed either by industry practitioners or by academic researchers who worked in collaboration with leading companies in technology, retail, manufacturing, and other sectors. In other words, TensorHouse focuses mainly on industry-proven methods and models rather than on theoretical research.

DQN learns a Hi-Lo pricing policy that switches between regular and discounted prices:

DQN learns how to control procurement and logistics in a simulated environment:

Deep autoencoders produce image reconstructions that facilitate detection of defect locations:

LLM dynamically writes a python script that invokes multiple APIs to answer user's question:

The artifacts listed in this section can help to rapidly evaluate different solution approaches and build prototypes using your datasets. Some artifacts can help with doing exploratory data analysis, e.g. evaluating the strength of causal effects in your data and determining whether these data is feasible for solving a certain use case or not.

These notebooks can be used to create customer propensity scoring, offer personalization, and budget/campaign optimization pipelines.

• Customer Scoring and Lifetime Value
  • Promotion Effect Estimation Using Causal Inference Methods (Regression and Matching) (notebook)
  • Customer Propensity Scoring Using Deep Learning (LSTM with Attention) (notebook)
  • Customer Lifetime Value (LTV) Modeling Using Markov Chain (notebook)
• Decision Automation
  • Dynamic Content Personalization Using Contextual Bandits (LinUCB) (notebook)
  • Next Best Action Model Using Reinforcement Learning (Fitted Q Iteration) (notebook)
• Media Mix, Attribution, and Budget Optimization
  • Media Mix Modeling: Basic Adstock Model for Campaign/Channel Attribution (notebook)
  • Media Mix Modeling: Bayesian Model with Carryover and Saturation Effects (notebook)
  • Multitouch Channel Attribution Model Using Deep Learning (LSTM with Attention) (notebook)

These notebooks can be used to create enterprise search, product catalog search, and visual search solutions.

• Text Search
  • Latent Semantic Analysis (LSA) (notebook)
  • Retrieval-augmented Generation (RAG) Using LLMs (notebook)
  • Retrieval-augmented Generation (RAG) Using LLM Agents (notebook)
• Visual Search
  • Visual Search by Artistic Style (VGG16) (notebook)
  • Visual Search based on Product Type (EfficientNetB0) (notebook)
  • Visual Search Using Variational Autoencoders (notebook)
  • Image Search Using a Language-Image Model (CLIP) (notebook)
• Structured Data Search
  • Relational Data Querying Using LLMs (notebook)
• Data Preprocessing
  • Product Attribute Discovery, Extraction, and Harmonization Using LLMs (notebook)

These notebooks can be used to prototype product recommendation solutions.

• Embedding Calculation
  • Item2Vec Model Using Word2vec (notebook)
  • Customer2Vec Model Using Doc2vec (notebook)
• Collaborative Filtering
  • Nearest Neighbor User-based Collaborative Filtering (notebook)
  • Nearest Neighbor Item-based Collaborative Filtering (notebook)
• Deep and Hybrid Recommenders
  • Neural Collaborative Filtering - Prototype (notebook)
  • Neural Collaborative Filtering - Hybrid Recommender (notebook)
  • Behavior Sequence Transformer (notebook)
  • Graph Recommender Using Node2Vec (notebook)

These notebooks can be used to create content analytics tools and pipelines.

• Sentiment Analysis Using Basic Transformers (notebook)
• Virtual Focus Groups Using LLMs (notebook)

These notebooks can be used to create demand and sales forecasting pipelines. These pipelines can further be used to solve inventory planning, price management, workforce optimization, and financial planning use cases.

• Traditional Methods
  • Demand Forecasting Using Exponential Smoothing (ETS) (notebook)
  • Demand Forecasting and Price Elasticity Analysis Using Time Series Regression (notebook)
• Deep Learning Methods
  • Demand Forecasting Using DeepAR (notebook)
  • Demand Forecasting Using NeuralProphet (notebook)
• Data Preprocessing
  • Demand Unconstraining (notebook)

These notebooks can be used to create price optimization, promotion (markdown) optimization, and assortment optimization solutions.

• Static Price, Promotion, and Markdown Optimization
  • Market Response Functions (notebook)
  • Price Optimization for Multiple Products (notebook)
  • Price Optimization for Multiple Time Intervals (notebook)
• Dynamic Pricing
  • Dynamic Pricing Using Thompson Sampling (notebook)
  • Dynamic Pricing with Limited Price Experimentation (notebook)
  • Bayesian Demand Models (notebook)
  • Price Optimization Using Reinforcement Learning (DQN) (notebook)

These notebooks and applications can be used to develop procurement and inventory allocation solutions, as well as provide supply chain managers with advanced decisions support and automation tools.

• Single-echelon Inventory Optimization Using (s,Q) and (R,S) Policies (notebook)
• Multi-echelon Inventory Optimization Using Reinforcement Learning (DDPG, TD3) (notebook)
• Inventory Allocation Optimization (notebook)
• Supply Chain Simulator for Reinforcement Learning Based Optimization (PPO) (notebook)
• Supply Chain Control Tower Using LLMs (notebook)

These notebooks can be used to prototype visual quality control and predictive maintenance solutions.

• Noise Reduction in Multivariate Timer Series Using Linear Autoencoder (PCA) (notebook)
• Remaining Useful Life Prediction Using Convolution Networks (notebook)
• Anomaly Detection in Time Series (notebook)
• Anomaly Detection in Images using Autoencoders (notebook)

These questionnaires can be used to assess readiness for typical AI/ML projects and collect the requirements for creating roadmaps and estimates.

• Demand Sensing and Forecasting (document)
• Price and Promotion Optimization (document)
• Next Best Action (document)

• Generic Regression and Classification Models

  • Neural Network with Vector Inputs (notebook)
  • Neural Network with Sequential Inputs (ConvNet, LSTM, Attention) (notebook)

• Enterprise Time Series Analysis

  • Forecasting Using ARIMA and SARIMA (notebooks 1 2)
  • Decomposition and Forecasting using Bayesian Structural Time Series (BSTS) (notebooks 1 2 3 4)
  • Forecasting and Decomposition using Gradient Boosted Decision Trees (GBDT) (notebook)
  • Forecasting and Decomposition using LSTM with Attention (notebook)
  • Forecasting and Decomposition using VAR/VEC models (notebooks 1 2)

• The most basic models are described the Introduction to Algorithmic Marketing.
  • Book's website - https://www.algorithmicmarketingbook.com/
• More advanced models that use deep learning and reinforcement learning techniques are described in The Theory and Practice of Enterprise AI.
  • Book's website - https://www.enterprise-ai-book.com/
• Most notebooks contain references to specific research papers, industrial reports, and real-world case studies.
• Follow LinkedIn and X (Twitter) for notifications about new developments and releases.