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Examples include Amazon SageMaker Notebooks, [9] Google's Colab, [10] [11] and Microsoft's Azure Notebook. [12] Visual Studio Code supports local development of Jupyter notebooks. As of July 2022, the Jupyter extension for VS Code has been downloaded over 40 million times, making it the second-most popular extension in the VS Code Marketplace. [13]
Notebook interfaces are widely used for statistics, data science, machine learning, and computer algebra. [ 2 ] At the notebook core is the idea of literate programming tools which "let you arrange the parts of a program in any order and extract documentation and code from the same source file.", [ 3 ] the notebook takes this approach to a new ...
An electronic lab notebook (also known as electronic laboratory notebook, or ELN) is a computer program designed to replace paper laboratory notebooks. Lab notebooks in general are used by scientists , engineers , and technicians to document research , experiments , and procedures performed in a laboratory.
IPython continued to exist as a Python shell and kernel for Jupyter, but the notebook interface and other language-agnostic parts of IPython were moved under the Jupyter name. [ 11 ] [ 12 ] Jupyter is language agnostic and its name is a reference to core programming languages supported by Jupyter, which are Julia , Python , and R .
A common use of Binder is for sharing a Jupyter notebook in a way that the recipient can immediately execute in a browser. [3] The Binder project maintains core libraries and documentation for running Binder services, which make those projects available, as well as BinderHub, a tool for deploying such services via common cloud computing ...
nanoHUB.org is a science and engineering gateway comprising community-contributed resources and geared toward education, professional networking, and interactive simulation tools for nanotechnology. [1] Funded by the United States National Science Foundation (NSF), it is a
Open-notebook science is the practice of making the entire primary record of a research project publicly available online as it is recorded. This involves placing the personal, or laboratory, notebook of the researcher online along with all raw and processed data, and any associated material, as this material is generated.
In data mining, k-means++ [1] [2] is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm. It was proposed in 2007 by David Arthur and Sergei Vassilvitskii, as an approximation algorithm for the NP-hard k-means problem—a way of avoiding the sometimes poor clusterings found by the standard k-means algorithm.