train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc

EconML/CausalML KDD 2021 Tutorial

Uplift modeling and causal inference with machine learning algorithms

This project contains some deep learning code

轩辕：度小满中文金融对话大模型

Project to analyze the 'BlogFeedback Data Set' from the UC Irvine Machine Learning repository

Code for the Make Your Own Neural Network book

A Python 3 package for state-of-the-art statistical dimension reduction methods

Learning Sparse Neural Networks through L0 regularization

Monotonic Networks – 1997, Sill

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

Improve marketing campaign of a Portuguese bank by analyzing their past marketing campaign data and recommending which customer to target

CausalLift: Python package for causality-based Uplift Modeling in real-world business

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Multiple Response Uplift (or heterogeneous treatment effects) package that builds and evaluates tradeoffs with multiple treatments and multiple responses

《统计学习方法》笔记-基于Python算法实现

温州大学《机器学习》课程资料（代码、课件等）

pytorch implementation of dragonnet

Undersampling and calibration for uplift modeling

Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.

爬虫小demo