A collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain
TensorHouse is a collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain, and more. The goal of the project is to provide baseline implementations for industrial, research, and educational purposes.
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.
TensorHouse contains the following resources: * a well-documented repository of reference model implementations, * a manually curated list of important papers in modern operations research, * a manually curated list of public datasets related to enterprise use cases.
Strategic price optimization using reinforcement learning: \ DQN learns a Hi-Lo pricing policy that switches between regular and discounted prices
Supply chain optimization using reinforcement learning: \ World Of Supply simulation environment
Anomaly detection in images using autoencoders
Promotions and Advertisements
Pricing and Assortment
Generic Regression and Classification Models
Enterpirse Time Series Analysis
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