tensor-house

Introduction to TensorHouse

TensorHouse is an innovative project offering a suite of reference Jupyter notebooks and demo applications in AI and ML. These resources are designed for enterprise applications, including marketing, pricing, supply chain management, and smart manufacturing. The central aim of TensorHouse is to equip businesses with a toolkit that facilitates rapid readiness assessments, exploratory data analysis, and the prototyping of various modeling techniques suited to typical enterprise projects in AI, ML, and data science.

Resources Offered by TensorHouse

TensorHouse provides several valuable resources to aid businesses in their AI/ML endeavors:

• Comprehensive Repository: A thoroughly documented repository of reference notebooks and demo applications to serve as prototypes.
• Assessment Tools: Readiness assessment tools and requirements gathering questionnaires tailored for common enterprise AI/ML projects.
• Data Prototyping and Evaluation: Access to datasets, data generators, and simulators that expedite prototyping and model assessments.

The solutions offered by TensorHouse lean heavily on industry-proven approaches, emphasizing deep learning, reinforcement learning, and causal inference. These techniques and models were initially developed by either industry professionals or academic researchers collaborating with leading companies across sectors such as technology, retail, and manufacturing.

Benefits of TensorHouse

TensorHouse streamlines several pivotal steps in solution development:

1. Readiness Evaluation: Expedite the evaluation of readiness for specific use cases using carefully crafted questionnaires and causal inference templates.

2. Method Selection: Select potential methods and models to address your use cases and refine them using simulators and sample datasets.

3. Prototype Building and Evaluation: Test candidate methods and models on your data, build prototypes, and present preliminary results to stakeholders.

Libraries Utilized by TensorHouse

The prototypes and templates in TensorHouse are created in Python, utilizing a set of standard libraries:

• Deep Learning: Primarily TensorFlow, with some prototypes using PyTorch.
• Reinforcement Learning: RLlib.
• Causal Inference: DoWhy, EconML.
• Probabilistic Programming/Bayesian Inference: PyMC.
• Generative AI: LangChain.
• Traditional Machine Learning: statsmodels, scikit-learn, LightGBM.
• Basic Libraries: NumPy, pandas, matplotlib, seaborn.

Illustrative Examples

TensorHouse showcases a series of practical examples demonstrating its capabilities:

• Strategic Price Optimization Using Reinforcement Learning: Deploying DQN to learn a Hi-Lo pricing policy, effectively switching between regular and discounted prices.
• Supply Chain Optimization: Reinforcement learning applied through DQN to manage procurement and logistics within a simulated environment.
• Supply Chain Management with Large Language Models: Using LLMs to dynamically generate a Python script that queries multiple APIs to address user questions.
• Anomaly Detection: Utilizing deep autoencoders to facilitate the detection of defects in images through their reconstructions.

Prototypes and Templates

TensorHouse offers multiple artifacts aiding in the evaluation of diverse solution approaches and prototype development using bespoke datasets. The artifacts include:

• Promotions and Advertisements: Tools for analyzing individual customer behavior, calculating propensity scores, and personalizing offers and content.
• Customer and Content Analytics: Resources for aggregated customer data analysis and marketing budget optimization.
• Search Solutions: Tools for enterprise search, product catalog, and visual search.
• Recommendations: Prototyping solutions for product recommendations.
• Demand Forecasting: Pipelines for demand and sales forecasting essential for inventory planning and price management.
• Pricing and Assortment: Solutions for optimizing prices and product assortments.
• Supply Chain Management: Advanced decision support tools for inventory allocation and procurement optimization.
• Smart Manufacturing: Prototypes for visual quality control and predictive maintenance.

Questionnaires and Further Documentation

TensorHouse provides questionnaires for assessing readiness and gathering requirements for AI/ML projects (like pricing and promotion optimization). Extensive documentation and educational resources are available, including references to research papers and case studies that enhance understanding and practical application of AI techniques.

Community and Contribution

TensorHouse welcomes community contributions, whether in the form of new use cases, advanced features, usability improvements, or enriched documentation. Contributors are vital to enhancing the project's scope and utility.

TensorHouse is not just a toolkit but a thriving community poised to propel enterprises into the future of AI and ML with tested, scalable solutions.