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This page was last edited on 26 November 2021, at 16:57 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
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]
NotebookLM (Google NotebookLM) is a research and note-taking online tool developed by Google Labs that uses artificial intelligence (AI), specifically Google Gemini, to assist users in interacting with their documents. It can generate summaries, explanations, and answers based on content uploaded by users.
العربية; অসমীয়া; Azərbaycanca; تۆرکجه; বাংলা; 閩南語 / Bân-lâm-gú; भोजपुरी; Bosanski; ChiShona; Corsu
Images, audio and video files must be uploaded into Wikipedia using the "Upload file" link on the left-hand navigation bar. Only logged in users can upload files. Once a file is uploaded, other pages can include or link to the file. Uploaded files are given the "File:" prefix by the system, and each one has an image description page.
Uploading can be used in the context of (potentially many) clients that send files to a central server. While uploading can also be defined in the context of sending files between distributed clients, such as with a peer-to-peer (P2P) file-sharing protocol like BitTorrent , the term file sharing is more often used in this case.
Google Cloud Platform is a part [8] of Google Cloud, which includes the Google Cloud Platform public cloud infrastructure, as well as Google Workspace (G Suite), enterprise versions of Android and ChromeOS, and application programming interfaces (APIs) for machine learning and enterprise mapping services.
# imports from jax import jit import jax.numpy as jnp # define the cube function def cube (x): return x * x * x # generate data x = jnp. ones ((10000, 10000)) # create the jit version of the cube function jit_cube = jit (cube) # apply the cube and jit_cube functions to the same data for speed comparison cube (x) jit_cube (x)