Whoeiy

Whoeiy's Stars

• MrChrisJohnson/logistic-mf

  Logistic Matrix Factorization for Implicit Feedback Data. http://stanford.edu/~rezab/nips2014workshop/submits/logmat.pdf

  Language:Python16072

• TeddyWang0202/recommendation_system

  recommendation system building with online article data and tutorial

  Language:Jupyter Notebook112

• benfred/implicit

  Fast Python Collaborative Filtering for Implicit Feedback Datasets

  Language:Python3.6k611

• apple/turicreate

  Turi Create simplifies the development of custom machine learning models.

  Language:C++11.2k1.1k

• chengstone/movie_recommender

  MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。

  Language:Jupyter Notebook1.3k477

• towardsai/tutorials

  AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

  Language:Jupyter Notebook1k361

• PacktPublishing/Building-Recommender-Systems-with-Machine-Learning-and-AI

  Building Recommender Systems with Machine Learning and AI, published by Packt

  Language:Jupyter Notebook10059

• proto-n/recsys-challenge-2018

  Solution for the 2018 Spotify RecSys Challenge

  Language:Python46

• NVIDIA/DALI

  A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.

  Language:C++5.2k624

• MatePocs/boardgame_recommendation

  Collaborative filtering recommendation system using individual user ratings data to recommend new items for individual users.

  Language:Jupyter Notebook189

• recommenders-team/recommenders

  Best Practices on Recommendation Systems

  Language:Python19.6k3.1k

• databricks/devrel

  This repository contains the notebooks and presentations we use for our Databricks Tech Talks

  Language:HTML702430

• ucalyptus/Spotify-Recommendation-Engine

  Music Recommender System

  Language:Jupyter Notebook245117

• NicolasHug/Surprise

  A Python scikit for building and analyzing recommender systems

  Language:Python6.5k1k

• aturbofly/crab

  Crab is a flexible, fast recommender engine for Python that integrates classic information filtering recommendation algorithms in the world of scientific Python packages (numpy, scipy, matplotlib).

  Language:Python159

• CavsZhouyou/Front-End-Interview-Notebook

  :ant:前端面试复习笔记

  7.5k1.6k

• vaslnk/Spotify-Song-Recommendation-ML

  UC Berkeley team's submission for RecSys Challenge 2018

  Language:Jupyter Notebook8622

• yzzhang/machine-learning

  Language:Jupyter Notebook5353

• databricks/koalas

  Koalas: pandas API on Apache Spark

  Language:Python3.3k358

• audreyloeffel/NetflixPrizeLike

  Language:Jupyter Notebook34

• mrthlinh/Spotify-Playlist-Recommender

  Playlist Recommender for Spotify

  Language:Python4110

• VasiliyRubtsov/recsys2018

  Language:Jupyter Notebook6723

• HarshdeepGupta/recommender_pytorch

  A recommender system in PyTorch from scratch.

  Language:Python11633

• allmachinelearning/MachineLearning

  Machine learning resources

  3.5k969

• NirantK/awesome-project-ideas

  Curated list of Machine Learning, NLP, Vision, Recommender Systems Project Ideas

  8k1.2k

• SethHWeidman/DLFS_code

  Code for the book Deep Learning From Scratch, from O'Reilly September 2019

  Language:Jupyter Notebook501279

• digininja/DVWA

  Damn Vulnerable Web Application (DVWA)

  Language:PHP10.5k3.7k

• haozi/xss-demo

  👮🏻‍♂️ XSS attack playground,there are answers in issues. XSS 攻防靶场，issues 有答案

  Language:JavaScript29859

• OWASP/wstg

  The Web Security Testing Guide is a comprehensive Open Source guide to testing the security of web applications and web services.

  Language:Dockerfile7.5k1.4k

• kentcdodds/testing-workshop

  A workshop for learning how to test JavaScript applications

  Language:JavaScript1.4k710