Popular GitHub Repositories for Hadoop

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

Alluxio, data orchestration for analytics and machine learning in the cloud

AI on Hadoop

TonY is a framework to natively run deep learning frameworks on Apache Hadoop.

本系统是我的毕业设计项目，题目为“基于用户画像的电影推荐系统的设计与实现”。主要是以Django作为基础框架，采用MTV模式，数据库使用MongoDB、MySQL和Redis，以从豆瓣平台爬取的电影数据作为基础数据源，主要基于用户的基本信息和使用操作记录等行为信息来开发用户标签，并使用Hadoop、Spark大数据组件进行分析和处理的推荐系统。管理系统使用的是Django自带的管理系统，并使用simpleui进行了美化。

Distributed Deep Learning, with a focus on distributed training, using Keras and Apache Spark.

CDP Public Cloud is an integrated analytics and data management platform deployed on cloud services. It offers broad data analytics and artificial intelligence functionality along with secure user access and data governance features.

Ytk-learn is a distributed machine learning library which implements most of popular machine learning algorithms(GBDT, GBRT, Mixture Logistic Regression, Gradient Boosting Soft Tree, Factorization Machines, Field-aware Factorization Machines, Logistic Regression, Softmax).

MorphL Community Edition uses big data and machine learning to predict user behaviors in digital products and services with the end goal of increasing KPIs (click-through rates, conversion rates, etc.) through personalization

An end-to-end machine learning and data mining framework on Hadoop

extremely distributed machine learning

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).

This project aims to build an enterprise-grade offline data warehouse solution based on e-commerce platform order data.

? Moodify - an emotion-based music recommendation system that uses AI/ML models to analyze text, speech, and facial expressions, providing personalized music recommendations across web and mobile platforms.

Cheatsheets on numerous topics ranging from DataScience | ML | DL | AI | Big Data.

? A scalable, production-ready data pipeline for real-time streaming & batch processing, integrating Kafka, Spark, Airflow, AWS, Kubernetes, and MLflow. Supports end-to-end data ingestion, transformation, storage, monitoring, and AI/ML serving with CI/CD automation using Terraform & GitHub Actions.

Data Analysis Using Hadoop/Spark/Storm/ElasticSearch/MachineLearning etc. This is My Daily Notes/Code/Demo. Don't fork, Just star !

Pyspark in Google Colab: A simple machine learning (Linear Regression) model

By Smart Shaped s.r.l. (https://www.smartshaped.com/)