TensorHouse is a collection of reference Jupyter notebooks and demo AI/ML applications designed for enterprise use cases across marketing, supply chain, manufacturing, and more. It targets data scientists and engineers seeking to accelerate readiness assessment, exploratory data analysis, and prototyping of industry-proven AI/ML solutions. The project provides a toolkit for evaluating and tailoring deep learning, reinforcement learning, and causal inference models to specific business problems.

How It Works

TensorHouse leverages Python with standard libraries like TensorFlow, PyTorch, RLlib, DoWhy, EconML, and scikit-learn to implement a wide array of enterprise AI patterns. Its approach emphasizes industry-proven solutions, often derived from collaborations between academic researchers and leading companies. The repository includes curated datasets, simulators, and readiness assessment questionnaires to facilitate rapid prototyping and model evaluation, bridging the gap between theoretical concepts and practical application.

Quick Start & Requirements

• Install: Primarily uses Python notebooks. Installation typically involves cloning the repository and setting up a Python environment with necessary libraries (e.g., pip install -r requirements.txt).
• Prerequisites: Python, TensorFlow, PyTorch, RLlib, DoWhy, EconML, scikit-learn, pandas, NumPy. Specific notebooks may have additional dependencies.
• Resources: Requires a Python environment and potentially significant computational resources for running deep learning and reinforcement learning models.
• Links: TensorHouseBasic repository for basic templates.

Highlighted Details

• Comprehensive coverage of enterprise AI use cases, including demand forecasting, pricing optimization, customer analytics, and supply chain management.
• Integration of advanced techniques like Reinforcement Learning (DQN, DDPG, TD3) for dynamic pricing and supply chain control.
• Application of Causal Inference (DoWhy, EconML) for campaign effect estimation and uplift modeling.
• Use of Large Language Models (LLMs) for tasks like dynamic script generation and virtual focus groups.

Maintenance & Community

The project is actively maintained by its contributors. Updates and new developments are announced via LinkedIn and X (Twitter). Contributions are welcomed.

Licensing & Compatibility

The repository's licensing is not explicitly stated in the README. Users should verify licensing for commercial use or integration into closed-source projects.

Limitations & Caveats

While providing reference implementations, many prototypes are marked as conceptual (🚀) and may not be directly productizable without further engineering. The project focuses on demonstrating specific techniques rather than offering a fully integrated, production-ready platform.