Zhang Le's repositories
r-on-azure
Compilation of R packages and tools for doing data science and AI on Azure cloud
yueguoguo.github.io
My site about data science, machine learning, and artificial intelligence.
ai-deadlines
:alarm_clock: AI conference deadline countdowns
architecture-center
Azure Architecture Center
aztk
On-demand, Dockerized, Spark Jobs on Azure (powered by Azure Batch)
azure-cosmosdb-spark
Apache Spark Connector for Azure Cosmos DB
azure-docs
Open source documentation of Microsoft Azure
azure-rest-api-specs
The source for REST API specifications for Microsoft Azure.
azure-sdk-for-python
This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/en-us/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.
codebase
Code base for programming projects
ComputerVision
Best Practices, code samples, and documentation for Computer Vision.
DataScienceVM
Tools and Docs on the Azure Data Science Virtual Machine (http://aka.ms/dsvm)
DeepRecommender
Deep learning for recommender systems
dintaifung
This is a repo about dumplings
educat
EduCAT - Educate every person and organization on the planet to achieve more - with Byte size education platform
MachineLearningNotebooks
Use the sample notebooks in this repo to explore the Azure Machine Learning service. Start with the 01.getting-started notebooks.
mmlspark
Microsoft Machine Learning for Apache Spark
Product-Recommendations
Product Recommendations solution
production-ready-recsys
A collection of production-ready recommendation system papers
pyute
Python Utils
recommender_with_chatgpt
Build a simple collaborative filtering recommender system with ChatGPT
recommonmark
A markdown parser for docutils
shopify-flask-example
A simple Shopify app created using Flask and Python
spark
Mirror of Apache Spark
support.996.ICU
Microsoft and GitHub Workers Support 996.ICU
tips
Some tips
xlearn
High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.