xiuxianxi

xiuxianxi's Stars

• ageron/handson-ml

  ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

  Language:Jupyter Notebook25.2k1.1k56312.9k

• meanjs/mean

  MEAN.JS - Full-Stack JavaScript Using MongoDB, Express, AngularJS, and Node.js -

  Language:JavaScript4.9k2941.1k2k

• thuquant/awesome-quant

  **的Quant相关资源索引

  4.1k3156888

• rasbt/python_reference

  Useful functions, tutorials, and other Python-related things

  Language:Jupyter Notebook3.8k23516937

• qiwsir/algorithm

  Language:Python3.3k234111.1k

• datastax/spark-cassandra-connector

  DataStax Connector for Apache Spark to Apache Cassandra

  Language:Scala1.9k1630917

• szilard/benchm-ml

  A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

  Language:R1.9k14845334

• AllenDowney/ThinkBayes

  Code repository for Think Bayes.

  Language:TeX1.6k127141.9k

• memect/hao

  好东西传送门

  1.4k252348461

• vega/datalib

  JavaScript data utility library.

  Language:JavaScript7324047133

• ColZer/DigAndBuried

  挖坑与填坑

  Language:GCC Machine Description6971134275

• Mendeley/mrec

  A recommender systems development and evaluation package by Mendeley

  Language:Python5637514174

• Lab41/sunny-side-up

  Sentiment Analysis Challenge

  Language:Jupyter Notebook5217411135

• timsainb/Tensorflow-MultiGPU-VAE-GAN

  A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.

  Language:Jupyter Notebook440181496

• lytforgood/MachineLearningTrick

  Machine Learning Trick : GBDT_Feature Blending Stacking CascadeForest

  Language:Python367223179

• huaxz1986/deep_learning_notes

  deeplearningbook学习笔记，来自于 http://www.deeplearningbook.org

  269324101

• jaceklaskowski/spark-workshop

  Apache Spark™ and Scala Workshops

  Language:HTML261310146

• daxiongshu/tradeshift-text-classification

  This is the 1st place solution of a kaggle machine contest: Tradeshift Text Classification. http://www.kaggle.com/c/tradeshift-text-classification

  Language:Python15014160

• szilard/teach-data-science-UCLA-master-appl-stats

  Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

  Language:HTML13524364

• lanxuezaipiao/movie-recommendation-system

  A movie recommendation system given by user data, movie data and social data...

  Language:Java7818068

• lcdm-uiuc/info490-sp17

  Advanced Data Science, University of Illinois Spring 2017

  Language:Jupyter Notebook5613077

• abajwa-hw/hdp-datascience-demo

  HDP Data Science/Machine Learning demo

  Language:HTML379135

• drangons/entity_resolution_spark

  Collection of some algorithms for entity resolution

  Language:HTML28706

• elenacuoco/bosch-kaggle-competition-spark

  Bosch Kaggle competion: Reduce manufacturing failures (https://www.kaggle.com/c/bosch-production-line-performance)

  Language:Python243013

• evah/CTR_Prediction

  Click through rate prediction

  21305

• AnilSener/Axa-Insurance-Telematics-Kaggle

  I developed this case study only in 7 days with Pyspark (Spark 1.6.0) SQL & MLlib. I used Databricks cluster and AWS. %90 AUC is achieved (without involving Trip Matching-Repeated Trips feature) with Random Forest. Many ensembles with RF, GBT and Logistic Regression and outlier elimination could be used to improve this result. There are two versions of my code (test and full execution). Since AWS costs have exceeded my budget I sopped to train my model(s) all dataset for full dataset execution. There is also a ppt that presents my outputs in test execution. Full Data Execution code is more production ready and slightly different version. I had to use Databricks Table Caching to TRAIN and TEST data tables to obtain acceptable performance in production ready version.

  Language:Jupyter Notebook163111

• logicalguess/tf-idf-spark-and-python

  TF-IDF with Spark for the Kaggle popcorn competition

  Language:Scala10306

• futurely/spark-kaggle

  Spark in Kaggle competitions

  Language:Scala9202

• dusanGrubjesic/Spark_Linear_Regression

  Spark (pyspark) linear regression on clickthrough rate (CTR) prediction form Kaggle

  Language:Python8303

• wilsonandusa/UI-Flix

  A movie recommandation website(course project)

  Language:CSS4102