YuhkiHuang

YuhkiHuang's Stars

• Avik-Jain/100-Days-Of-ML-Code

  100 Days of ML Coding

  44k2.4k010.4k

• afshinea/stanford-cs-229-machine-learning

  VIP cheatsheets for Stanford's CS 229 Machine Learning

  17.3k708263.9k

• microsoft/LightGBM

  A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

  Language:C++16.4k4353.3k3.8k

• rougier/numpy-100

  100 numpy exercises (with solutions)

  Language:Python11.9k207825.7k

• guipsamora/pandas_exercises

  Practice your pandas skills!

  Language:Jupyter Notebook10.5k309658.1k

• yzhao062/pyod

  A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques

  Language:Python8.3k1443331.4k

• ljpzzz/machinelearning

  My blogs and code for machine learning. http://cnblogs.com/pinard

  Language:Jupyter Notebook8.2k303243.7k

• apachecn/sklearn-doc-zh

  :book: [译] scikit-learn（sklearn） 中文文档

  Language:CSS5.1k211511.5k

• Jack-Lee-Hiter/AlgorithmsByPython

  算法/数据结构/Python/剑指offer/机器学习/leetcode

  Language:Python3.9k123131.3k

• Avik-Jain/100-Days-of-ML-Code-Chinese-Version

  Chinese Translation for Machine Learning Infographics

  Language:Python3.3k2559919

• luhuisicnu/The-Flask-Mega-Tutorial-zh

  翻译自Miguel Grinberg的blog https://blog.miguelgrinberg.com 的2017年新版The Flask Mega-Tutorial教程

  3.3k12526891

• budaLi/-Learning-materials-

  各种学习资料，包括一些百度云视频链接还有pdf资料 --搬运工

  1.4k774517

• zhanghe06/python

  Python使用记录

  Language:Python484847303

• Dengshunge/Target-Offer

  根据《剑指offer》中的题目，用python来进行编写

  Language:Python942144

• AcidLeroy/MusicRecommender

  Music Recommendation using Python + Spark

  Language:Python23409

• Huayet/guidetodatamining

  面向程序员的数据挖掘指南

  Language:Python1212

• hu-guanwei/Movie-Recommender

  基于项目相似度的协同过滤推荐引擎

  Language:Jupyter Notebook101029

• tianqinglei/Spark

  Spark大数据技术交流,pySpark机器学习

  Language:Python50

• gargi/Music-Recommender-Using-Spark

  Music Recommender System which suggests artists to user based on their Listening History

  Language:Jupyter Notebook3000

• prachi108/Million-Song-Project

  Built a Song Recommendation System using both content-based and collaborative filtering on 10GB subset of MSD (Million Song Data) with a precision of 6.1%. Also performed trend analysis and visualization on MSD across the years, varying from song popularity to loudness. Used: Apache Spark, Python

  Language:Jupyter Notebook3

• zy2280/PySpark-Recommender-System

  Language:Jupyter Notebook300

• abhinandanramesh/Music-Recommendation-system

  Music Recommendation System using Apache Spark and Python

  Language:Jupyter Notebook26

• johnlbellamy/LastFM_Recommender

  An artist recommender made using PySpark and the LastFM 360k User Dataset; ALS

  Language:Jupyter Notebook210

• pavansvn/Million-Song-Recommendation-Engine

  Popularity Based & Item Based Collaborative Filtering

  Language:Jupyter Notebook20

• ananthjan/artistRecommendation

  Language:Jupyter Notebook1

• cmirallesp/music_recommender

  Repository to implement a music recommender based on lastfm data

  Language:Jupyter Notebook12

• EHDEV/spark_music

  Artist recommendation tool using pyspark

  Language:Jupyter Notebook1100

• farhanafayez/PySpark_MovieRecommendationSystem_ML

  A recommender that uses the movielens data set. It is similar to using the iris or MNIST data set for other algorithms.

  Language:Jupyter Notebook11

• peimengsui/MachineLearningCourseProject

  Language:Python1201

• YuhkiHuang/Anomaly-Intrusion-Detection

  Language:Python1100