Why did I create this project:
- There are many projects that summarize machine learning materials and courses, but there are not many projects that summarize machine learning tools.
- I read 《ARTIFICIAL INTELLIGENCE 101》, which introduces some tools. So an idea hit me that organizes more useful tools from time to time.
- Google Colab: Online deep learning platform.
- JupyterLab: Enhanced version of Jupyter Notebook.
- DeepmindLab: AI training experimental platform.
- Nextjournal: Experimental platform with templates, multi-language, version control, multi-person collaboration, GPU available, online help and other functions.
- xg2xg: A collection of development tools summarized by Googler.
- aiXcoder: Tensorflow code auto-complete plugin (for PyCharm).
- nbextensions: Jupyter Notebook plugin collection.
- jupytext: Support multi-format export, Jupyter Notebook and IDE jointly develop tool.
- Awesome-pytorch-list: Pytorch common library collection.
Dataset Annotation Tools
- LabelImg: Target detection (picture) annotation tool.
- Colabeler: Support for various annotation forms such as image/text/video.
- LC-Finder: Image management tool that supports image annotation and target detection.
- Tensorflow Playground: One of the Tensorflow toolset, using a browser to experience neural networks.
- TensorSpace: A 3D visualization framework for building neural networks.
- Embedding Projector: Google's open source high-dimensional data visualization tool.
- Netron: Model structure visualization.
- PythonTutor: Code visualization (Python, JS, Ruby, etc.).
- Visualgo: Data structure visualization.
- USF/Algomation/Algorithm Visualizer: Algorithm visualization.
- Graph Editor: Graph theory board.
- Netscope: Quickly draw neural network structures (flexibly).
- NN SVG: Quickly plot neural networks for FCNN, LeNet, and AlexNet (SVG version).
- ConvNetDraw: Quick Draw CNN (low resolution).
- PlotNeuralNet: Neural network drawing code (LaTeX).
- draw_convnet: Neural network drawing code (Python).
- Awesome Data Science with Python: Use Python to play data science resources (libraries, handouts, code snippets, blogs, etc.)
- Data Science Cheatsheets: List of Data Science Cheatsheets.
- Ubuntu Pastebin: Publish the code for easy reading.
- Try It Online: An online compiler that can share code and support hundreds of languages.
- Dooccn: Online compiler for various common languages.
- CodeIf: Let your variable naming no longer tangled.
- Datasetlist: Collect various data sets such as CV, NLP, QA, and Audio.
- Dataset Search: Google's dataset search engine.
- Awesome-public-dataset: A topic-centric list of HQ open datasets.
- RecommenderSystem-DataSet: Recommended System dataset.
- Data-SNA: Datasets for Social Network Analysis.
- Ai-Yanxishe: The data set (various types) collected by the AI Institute.
- Aistudio-dataset: A public data set aggregated by Baidu.
- Kaggle: Kaggle dataset.
- TensorFlow: The data set provided by Tensorflow.
- OpenCorporates: The world's largest company open dataset.
- Datagv(U.S.): US government open data.
- Datagv(U.K.): UK government open data.
- Health Data: Medical sanitation data set.
- CDC: Health disease control data set.
- The World Factbook: Information data for countries around the world.
- Pew Internet: Sociological data set.
- Papers with code: Find the paper corresponding to open source code.
- Papers with code (Sorted by stars): All papers from the AI field from 2013-2018 were collected and sorted according to the number of stars on GitHub.
- bestofml: Selected books/courses/recruitment sites/news blogs/papers in the field of machine learning.
- arXiv: The pre-printed website of the paper.
- arxiv-sanity-preserver: Arxiv paper classification, search and filtering.
- Overleaf: Online LaTeX editor.
- autoreject: Automatically generate paper review comments.
Note: If you are interested in this project, please keep your attention and welcome to complete it.