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* [Data Science Ipython Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks), [Scipy Lectures](http://scipy-lectures.org/) or [Virgilio](https://virgili0.github.io/Virgilio/) / [GitHub](https://github.com/virgili0/Virgilio) - Learn DataScience
* [Data Engineering Practice Problems](https://github.com/danielbeach/data-engineering-practice) - Data Engineering Practice
* [Learn to Make Netflix Clone](https://github.com/kubowania/netflix-clone-graphql-datastax)
* [100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) or [Practical Deep Learning](https://course.fast.ai/) - Machine Learning Coding Lessons
* [minitorch](https://github.com/minitorch/minitorch) - Machine Learning Engineering Course Code
* [Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf) - Machine Learning Problem Solving Book
* [ML-Youtube-Courses](https://github.com/dair-ai/ML-YouTube-Courses) - Machine Learning Courses on YouTube
* [PromptPapers](https://github.com/thunlp/PromptPapers) - Pre-Trained Language Model Tuning Papers
***
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* [Frontend Learning Kit](https://github.com/sadanandpai/frontend-learning-kit) - Frontend Learning Index
* [Frontend Bootcamp](https://github.com/microsoft/frontend-bootcamp) - Frontend Workshop
* [BitsOfCode](https://bitsofco.de/) - Frontend Dev Articles
* [Gaultier Blog](https://gaultier.github.io/blog/x11_x64.html) - Learn x86-64 Assembly
* [Design Pattern for Humans](https://github.com/kamranahmedse/design-patterns-for-humans) - Guide on Design Patterns
***
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* [XunLei](https://www.xunlei.com/) - Shared Computing / Blockchain
* [Wokwi](https://wokwi.com/) - Arduino / ESP32 Board Simulator
* [Tesseract](https://tesseract-ocr.github.io/) - Open Source OCR Engine / [GitHub](https://github.com/tesseract-ocr/tesseract)
* [awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes) or [ML_Resources](https://github.com/tunguz/ML_Resources) - Machine Learning Resources
* [awesome-google-colab](https://github.com/firmai/awesome-google-colab) - Machine Learning Colabs
* [Caffee](https://caffe.berkeleyvision.org/) / [Git](https://github.com/BVLC/caffe), [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning) or [OpenNN](https://www.opennn.net/) / [Git](https://github.com/Artelnics/OpenNN) - Machine Learning Framework
* [TensorFlow](https://www.tensorflow.org/) - Create Machine Learning Models
* [Awesome Opensource Data Engineering](https://github.com/gunnarmorling/awesome-opensource-data-engineering) - Open Source Data Engineering Projects
* [CLIP-as-service](https://github.com/jina-ai/clip-as-service) - Embed Images / Sentences via CLIP Models
* [Catalyzex](https://www.catalyzex.com/), [Feedly AI](https://feedly.com/ai) or [PapersWithCode](https://paperswithcode.com/) - Search Machine Learning Models / Papers
* [OpenML](https://www.openml.org/) or [Hugging Face](https://huggingface.co/) - Machine Learning Datasets
* [Kobra](https://kobra.dev/) - Visual Programming Language for Machine Learning
* [DVC](https://dvc.org/) - Machine Learning Version Control
* [DialoGPT](https://github.com/microsoft/DialoGPT) - Dialogue Machine Learning Model
* [MMDeploy](https://github.com/open-mmlab/mmdeploy) - Deep Learning Model Deployment Toolset
* [DeepSpeed](https://github.com/microsoft/DeepSpeed) - Deep Learning Optimization Library
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for Neural Network, Deep Learning, and Machine Learning Models
* [Cyberbotics](https://cyberbotics.com/) - Robot Simulator
* [OS Dev](https://wiki.osdev.org/) - Operation System Development Wiki
* [devSwag](https://devswag.io/) - Developer Giveaways
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* ⭐ **[XDA](https://forum.xda-developers.com/)** - App Development Forum
* ⭐ **[Docker](https://www.docker.com/)** - Build, Manage and Run Apps in Containers
* ⭐ **[Docker Tools](https://www.reddit.com/r/FREEMEDIAHECKYEAH/wiki/storage#wiki_docker_tools)** - Docker Tools, Images and Clients
* [SupaBase](https://supabase.com/), [Kodular](https://www.kodular.io/), [Glide](https://www.glideapps.com/), [AppCreator24](https://www.appcreator24.com/), [Dokku](https://dokku.com/), [AndroidIDE](https://androidide.com/), [Flutter](https://flutter.dev/) / [Resources](https://github.com/Solido/awesome-flutter), [2](https://github.com/leanflutter/awesome-flutter-desktop) or [Android Studio](https://developer.android.com/studio/index.html) - App Creators
* [SupaBase](https://supabase.com/), [Kodular](https://www.kodular.io/), [Glide](https://www.glideapps.com/), [AppCreator24](https://www.appcreator24.com/), [Dokku](https://dokku.com/), [AndroidIDE](https://androidide.com/), [Flutter](https://flutter.dev/) / [Resources](https://github.com/Solido/awesome-flutter), [2](https://github.com/leanflutter/awesome-flutter-desktop), [noodl](https://www.noodl.net/) or [Android Studio](https://developer.android.com/studio/index.html) - App Creators
* [Android Libhunt](https://android.libhunt.com/) - Android Packages
* [StreamLit](https://streamlit.io/) - Data Science App Creator / [Templates](https://extras.streamlit.app/) / [UI](https://github.com/hayabhay/whisper-ui)
* [Xamarin](https://dotnet.microsoft.com/en-us/apps/xamarin) - App Development Platform / [GitHub](https://github.com/xamarin)
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* [gitignore](https://github.com/github/gitignore) - Gitignore Templates
* [OpenHub](https://github.com/ThirtyDegreesRay/OpenHub), [diohub](https://github.com/NamanShergill/diohub), [Fasthub-Libre](https://github.com/thermatk/FastHub-Libre), [FastHub-RE](https://github.com/LightDestory/FastHub-RE), [MGit](https://github.com/maks/MGit), [GitNex](https://codeberg.org/gitnex/GitNex/) or [OctoDroid](https://github.com/slapperwan/gh4a) - Android GitHub Clients
* [GithubVisualizer](https://github.com/DheerajKotwani/GithubVisualizer) - Track Github User Activity on Android
* [Gitea](https://gitea.io/), [2](https://gittea.dev/) or [Drift](https://www.drift.lol/) - Self-Hosted Git Service
* [Gitea](https://gitea.io/) / [2](https://gittea.dev/) or [Drift](https://www.drift.lol/) - Self-Hosted Git Services
***
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* [JSPyBride](https://github.com/extremeheat/JSPyBridge) - Interoperate Node.js and Python
* [adb_shell](https://github.com/JeffLIrion/adb_shell) - Python ADB + Fastboot Implementation
* [pipx](https://pypa.github.io/pipx/) - Execute Python Binaries in Isolated Environments
* [Scikit Learn](https://github.com/scikit-learn/scikit-learn) - Machine Learning Library for Python
* [Pytrait](https://github.com/xrudelis/pytrait) - Python Interface Traits
* [PyPy](https://www.pypy.org/) - Alt Python Implementation
* [Doppler](https://www.doppler.com/) - Sync Environment Variables
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* [asdf](https://asdf-vm.com/) - Multi-Runtime Management CLI
* [The SHA-256 Project](https://github.com/oconnor663/sha256_project) - Develop SHA-256 from Scratch
* [substitute](https://github.com/comex/substitute) - Free Runtime Modification Library
* [Awesome Machine Learning Art](https://github.com/vibertthio/awesome-machine-learning-art) - Machine Learning Art Resource Index
* [Sourcegraph](https://sourcegraph.com/) or [Ag](https://geoff.greer.fm/ag/) / [GitHub](https://github.com/ggreer/the_silver_searcher) - Code Searching
* [searchcode](http://searchcode.com/) or [PublicWWW](http://publicwww.com/) - Source Code Search
* [Sourcecodester](https://www.sourcecodester.com/), [Code Projects](https://www.code-projects.org/), [Codastro](https://codeastro.com/), [Kashipara](https://www.kashipara.com/) or [ItSourceCode](https://www.itsourcecode.com/) - Source Code Projects
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## ▷ Machine Learning
* ⭐ **[Hugging Face](https://huggingface.co/)** or [OpenML](https://www.openml.org/) - Machine Learning Datasets
* ⭐ **[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)**, [Caffee](https://caffe.berkeleyvision.org/) / [Git](https://github.com/BVLC/caffe), [SuperAGI](https://github.com/TransformerOptimus/SuperAGI) or [OpenNN](https://www.opennn.net/) / [Git](https://github.com/Artelnics/OpenNN) - Machine Learning Frameworks
* ⭐ **[awesome-list-of-awesomes](https://github.com/Nachimak28/awesome-list-of-awesomes)**, [awesome-marketing-datascience](https://github.com/underlines/awesome-marketing-datascience/), [awesome-decentralized-llm](https://github.com/imaurer/awesome-decentralized-llm), [CyberCowboy](https://local-llm.cybercowboy.de) or [ML_Resources](https://github.com/tunguz/ML_Resources) - Machine Learning Resources
* [TensorFlow](https://www.tensorflow.org/) - Create Machine Learning Models
* [Catalyzex](https://www.catalyzex.com/) or [PapersWithCode](https://paperswithcode.com/) - Search Machine Learning Models / Papers
* [awesome-google-colab](https://github.com/firmai/awesome-google-colab) - Machine Learning Colabs
* [OpenAIPlayground](https://github.com/nat/openplayground)
* [OpenML](https://www.openml.org/) or [Hugging Face](https://huggingface.co/) - Machine Learning Datasets
* [DVC](https://dvc.org/) - Machine Learning Version Control
* [DeepSpeed](https://github.com/microsoft/DeepSpeed) - Deep Learning Optimization Library
* [FlowiseAI](https://flowiseai.com/) - LLM Flow Visualization
* [TeachableMachine](https://teachablemachine.withgoogle.com/) or [TensorFlow](https://www.tensorflow.org/) - Create Machine Learning Models
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for Neural Network, Deep Learning, and Machine Learning Models
* [Scikit Learn](https://github.com/scikit-learn/scikit-learn) - Machine Learning Library for Python
* [Awesome Machine Learning Art](https://github.com/vibertthio/awesome-machine-learning-art) - Machine Learning Art Resource Index
* [Catalyzex](https://www.catalyzex.com/), [Feedly AI](https://feedly.com/ai) or [PapersWithCode](https://paperswithcode.com/) - Search Machine Learning Models / Papers
* [Kobra](https://kobra.dev/) - Visual Programming Language for Machine Learning
* [DVC](https://dvc.org/) - Machine Learning Version Control
* [DialoGPT](https://github.com/microsoft/DialoGPT) - Dialogue Machine Learning Model
* [MMDeploy](https://github.com/open-mmlab/mmdeploy) - Deep Learning Model Deployment Toolset
* [DeepSpeed](https://github.com/microsoft/DeepSpeed) - Deep Learning Optimization Library
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for Neural Network, Deep Learning, and Machine Learning Models
* [Cyberbotics](https://cyberbotics.com/) - Robot Simulator
* [Dataset Card for Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca) or [awesome-instruction-dataset](https://github.com/yaodongC/awesome-instruction-dataset) - Language Model Training Datasets
* [PromptPapers](https://github.com/thunlp/PromptPapers) - Pre-Trained Language Model Tuning Papers
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* [MetaAcademy](https://metacademy.org/) - Machine Learning Guides
* [ML-Youtube-Courses](https://github.com/dair-ai/ML-YouTube-Courses) - Machine Learning Courses on YouTube
* [ML Course Notes](https://github.com/dair-ai/ML-Course-Notes) - Machine Learning Course Notes
* [100-Days-Of-ML-Code](https://github.com/Avik-Jain/100-Days-Of-ML-Code) or [Practical Deep Learning](https://course.fast.ai/) - Machine Learning Coding Lessons
* [minitorch](https://github.com/minitorch/minitorch) - Machine Learning Engineering Course Code
* [Approaching (Almost) Any Machine Learning Problem](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf) - Machine Learning Problem Solving Book
* [ML-Youtube-Courses](https://github.com/dair-ai/ML-YouTube-Courses) - Machine Learning Courses on YouTube
* [PromptPapers](https://github.com/thunlp/PromptPapers) - Pre-Trained Language Model Tuning Papers