ipynb

Open-source projects categorized as ipynb
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Language: + Python + Jupyter Notebook + Vim Script
Topics: Python jupyter-notebook colab-notebook Jupyter Machine Learning
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Top 8 ipynb Open-Source Projects

  • mlcourse.ai

    Open Machine Learning Course

    • Project mention: Open Machine Learning Course | news.ycombinator.com | 2023-10-22

  • machine_learning_basics

    Plain python implementations of basic machine learning algorithms

  • InfluxDB

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  • euporie

    Jupyter notebooks in the terminal

    • Project mention: I'm building a new web browser | /r/Python | 2023-05-23

    Currently it's part of euporie-notebook, but I'm planning on splitting it out and publishing the web-browser as an independent project.

  • pygraphblas

    GraphBLAS for Python

    • Project mention: Large Photonic Processor Solves Graph Problems | news.ycombinator.com | 2023-05-10

    This is interesting to me because it's advancing the work on the notion of quantum graph problem solving.

    I'm sure we've all heard how quantum computers can be used in the future to decrypt information from today. There's a lot of research out there on how QC may be able to efficiently factor large semiprimes and bust our existing cryptographic algorithms, but to me this is the more mundane side of QC.

    The exciting side to me is that many graph problems, particularly whole graph problems like connectivity and shortest paths have a potential quantum advantage. This is particularly advantageous for sparse and hypersparse graphs that have billions of nodes but relatively low node degree. Language Models, chemical assay databases, proteomics, causal inference, and fraud detection are just a few problems that involve huge sparse graphs that could get a huge boost from quantum.

    And to show my own bias here [1], I think the future of graph algorithms, including quantum, is expressing them in Linear Algebraic form with the GraphBLAS API. Using the GraphBLAS, you can write your algorithm in a mathematical form using the multiplication of adjacency matrices that is then synthesized to some optimal form for a given architecture.

    The same code you write can then be run on a variety of backends, currently CPUs and CUDA using SuiteSparse's new JIT, but soon FPGAs and yes, quantum computers. Parallelism will become so broad and conceptually divergent that you won't even be able to conceive of an efficient hand written single function for all possible platforms.

    [1] https://github.com/Graphegon/pygraphblas

  • jupytext.vim

    Vim plugin for editing Jupyter ipynb files via jupytext

  • 1filellm

    Specify a github or local repo, github pull request, arXiv or Sci-Hub paper, Youtube transcript or documentation URL on the web and scrape into a text file and clipboard for easier LLM ingestion

    • Project mention: Show HN: FileKitty – Combine and label text files for LLM prompt contexts | news.ycombinator.com | 2024-05-01

    I created something similar, https://github.com/jimmc414/1filellm

    It converts papers, repositories, PRs and web docs into one text file for llm ingestion

  • ImageAI-Setup-Guide

    This is a step-by-step guide on how to set up ImageAI using Google's free service, Google Collab

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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  • pre-commit-jupyter

    Clean up cell outputs of jupyter notebook

NOTE: The open source projects on this list are ordered by number of github stars. The number of mentions indicates repo mentiontions in the last 12 Months or since we started tracking (Dec 2020).

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Index

What are some of the best open-source ipynb projects? This list will help you:

Project Stars
1 mlcourse.ai 9,411
2 machine_learning_basics 4,211
3 euporie 1,462
4 pygraphblas 338
5 jupytext.vim 294
6 1filellm 224
7 ImageAI-Setup-Guide 33
8 pre-commit-jupyter 19

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