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.
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A reactive notebook for Python — run reproducible experiments, query with SQL, execute as a script, deploy as an app, and version with git. Stored as pure Python. All in a modern, AI-native editor.
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
Ready-to-run Docker images containing Jupyter applications
Multi-user server for Jupyter notebooks
Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
📚 Parameterize, execute, and analyze notebooks
Voilà turns Jupyter notebooks into standalone web applications
An open source extension that connects AI agents to computational notebooks in JupyterLab.
Impress your boss and turn a Jupyter notebook into a beautiful, shareable web app — no callbacks, no frontend, no rewrite.
An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks
Elyra extends JupyterLab with an AI centric approach.
A system for assigning and grading notebooks
Run ruff, isort, pyupgrade, mypy, pylint, flake8, and more on Jupyter Notebooks
Chapyter: ChatGPT Code Interpreter in Jupyter Notebooks
Google maps for Jupyter notebooks
A py.test plugin to validate Jupyter notebooks
Pytest in IPython notebooks.
Easy to use test framework for Jupyter Notebooks
Python game programming in Jupyter notebooks.
Jupyter Notebooks as plain Python code with embedded Markdown text
Tools to use a collection of notebooks as 'chapters'
Cell-by-cell testing for production Jupyter notebooks in JupyterLab
Automatically version jupyter notebooks in JupyterLab