shaunstanislauslau Goto Github PK
Name: Shaun Stanislaus
Type: User
Bio: A Fullstack Developer / Devops
Location: Singapore
Name: Shaun Stanislaus
Type: User
Bio: A Fullstack Developer / Devops
Location: Singapore
A collection of infrastructure and tools for research in neural network interpretability.
Luckysheet is an online spreadsheet like excel that is powerful, simple to configure, and completely open source.
Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code.
LuLu is the free open-source macOS firewall that aims to block unauthorized (outgoing) network traffic
LuLu UI for PC web
An alternative BEAM implementation, designed for WebAssembly
An experimental Lua VM implemented in pure Rust
LUX - Hybrid PoW/PoS & Unique PHI2 Algorithm | Masternode | Parallel masternode | Segwit | Smartcontract | Luxgate | Proof of file storage (Decentralised distributed file storage)
Python API for Intelligent Visual Data Discovery
Склонение названий городов, определения пола по ФИО, склонения имен по падежам
Experimental, scalable, high performance HTTP server
Lynis - Security auditing tool for Linux, macOS, and UNIX-based systems. Assists with compliance testing (HIPAA/ISO27001/PCI DSS) and system hardening. Agentless, and installation optional.
📦 Node.js to browser - The easy way
Transform ML models into a native code (Java, C, Python, etc.) with zero dependencies
M3 monorepo - Distributed TSDB and Query Engine, Prometheus Sidecar, Metrics Platform
MAC address age tracking
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
机器学习原理
Classical equations and diagrams in machine learning
:speech_balloon: Machine Learning Course with Python
A complete daily plan for studying to become a machine learning engineer.
周志华《机器学习》又称西瓜书是一本较为全面的书籍,书中详细介绍了机器学习领域不同类型的算法(例如:监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等),记录了本人在学习过程中的理解思路与扩展知识点,希望对新人阅读西瓜书有所帮助!
A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
A complete ML study path, focused on TensorFlow and Scikit-Learn
A booklet on machine learning systems design with exercises
MACHINE LEARNING YEARNING BY ANDREW NG
ML.NET is an open source and cross-platform machine learning framework for .NET.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.