lewisbakkero Goto Github PK
Name: lewisbakkero
Type: User
Name: lewisbakkero
Type: User
Source code and data sets for Going Deeper with Deep Knowledge Tracing (EDM-2016)
A list of resources used in Applied Data Science
Anomaly detection related books, papers, videos, and toolboxes
An implementation of the TKDE paper "Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising"
This repository illustrates some work in using GLMs to price car insurance based on car insurance policy and claim data. It also features some systematic data exploration and the use of MonteCarlo simulation to investigate the effectiveness of the pricing policies we are using.
fastai to categorise and predict on tabular data
Uplift modeling and causal inference with machine learning algorithms
A SQL interface for the CHILDES child language corpora
OpenCypher to SQL Mapper
A python library for easy manipulation and forecasting of time series.
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
Real data science interview assignments
Course material for Data Visualization course at Skidmore College
It is the DKT+ model implemented in python3 and tensorflow1.2
This repo contain the syllabus of the Hugging Face Deep Reinforcement Learning Class.
source code for the paper Deep Knowledge Tracing
Docker image supporting fastai, pytorch, tensorflow, jupyter, and anaconda.
Our implementation of the LSTM version of Deep Knowledge Tracing (DKT)
Deep learning for flexible market price modeling (landscape forecasting) in real-time bidding advertising. An implementation of our KDD 2019 paper with some other (Python) implemented prediction models.
A bunch of docker files to toy around
This reposity holds the code for paper Online Academic Course Performance Prediction using Relational Graph Convolutional Neural Network
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Official Code for DragGAN (SIGGRAPH 2023)
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
development of the next version of fastai
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.