Jupyter Notebook Research

Open-source Jupyter Notebook projects categorized as Research
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Top 16 Jupyter Notebook Research Projects

  • google-research

    Google Research

    • Project mention: Show HN: Next-token prediction in JavaScript – build fast LLMs from scratch | news.ycombinator.com | 2024-04-10

    People on here will be happy to say that I do a similar thing, however my sequence length is dynamic because I also use a 2nd data structure - I'll use pretentious academic speak: I use a simple bigram LM (2-gram) for single next-word likeliness and separately a trie that models all words and phrases (so, n-gram). Not sure how many total nodes because sentence lengths vary in training data, but there are about 200,000 entry points (keys) so probably about 2-10 million total nodes in the default setup.

    "Constructing 7-gram LM": They likely started with bigrams (what I use) which only tells you the next word based on 1 word given, and thought to increase accuracy by modeling out more words in a sequence, and eventually let the user (developer) pass in any amount they want to model (https://github.com/google-research/google-research/blob/5c87...). I thought of this too at first, but I actually got more accuracy (and speed) out of just keeping them as bigrams and making a totally separate structure that models out an n-gram of all phrases (e.g. could be a 24-token long sequence or 100+ tokens etc. I model it all) and if that phrase is found, then I just get the bigram assumption of the last token of the phrase. This works better when the training data is more diverse (for a very generic model), but theirs would probably outperform mine on accuracy when the training data has a lot of nearly identical sentences that only change wildly toward the end - I don't find this pattern in typical data though, maybe for certain coding and other tasks there are those patterns though. But because it's not dynamic and they make you provide that number, even a low number (any phrase longer than 2 words) - theirs will always have to do more lookup work than with simple bigrams and they're also limited by that fixed number as far as accuracy. I wonder how scalable that is - if I need to train on occasional ~100-word long sentences but also (and mostly) just ~3-word long sentences, I guess I set this to 100 and have a mostly "undefined" trie.

    I also thought of the name "LMJS", theirs is "jslm" :) but I went with simply "next-token-prediction" because that's what it ultimately does as a library. I don't know what theirs is really designed for other than proving a concept. Most of their code files are actually comments and hypothetical scenarios.

    I recently added a browser example showing simple autocomplete using my library: https://github.com/bennyschmidt/next-token-prediction/tree/m... (video)

    And next I'm implementing 8-dimensional embeddings that are converted to normalized vectors between 0-1 to see if doing math on them does anything useful beyond similarity, right now they look like this:

      [nextFrequency, prevalence, specificity, length, firstLetter, lastLetter, firstVowel, lastVowel]

  • datasets

    🎁 5,400,000+ Unsplash images made available for research and machine learning (by unsplash)

    • Project mention: AI-Powered Image Search with CLIP, pgvector, and Fast API | dev.to | 2024-02-12

    Here's a live demo with a simple React frontend. It's searching against an S3 bucket containing Unsplash's open source dataset of 25,000 images, plus a few of my own.

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • hyperlearn

    2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

    • Project mention: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning | news.ycombinator.com | 2023-12-01

    Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn).

    I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community.

    The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work.

    I also used to accept and help everyone with their writing their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place.

    So after a sad awakening, I decided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable.

    I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no?

    If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!

  • awesome-data-centric-ai

    Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖

    • Project mention: Thoughts: Continue current degree with one year left, or start anew with degree apprenticeship | /r/cscareerquestionsuk | 2023-07-13

    I would finish the degree anyway. It's only one year left. If teachers miss classes, I would disregard that and try to learn on my own, and then yes, I would move on to an internship (or even do It at the same time if it's possible). If you like, come as meet us at the Data-Centric AI Community and we can do some projects together :)

  • image-crop-analysis

    Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

  • goodreads

    code samples for the goodreads datasets (by MengtingWan)

  • benford_py

    Python implementation of Benford's Law tests.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

    InfluxDB logo

  • mnist1d

    A 1D analogue of the MNIST dataset for measuring spatial biases and answering Science of Deep Learning questions.

  • deepcourse

    Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

  • cobaltstrike-beacon-data

    Open Dataset of Cobalt Strike Beacon metadata (2018-2022)

  • threat-research-and-intelligence

    BlackBerry Threat Research & Intelligence

  • tnb-analysis

    Gain insights about thenewboston digital crypto currency network by doing some analysis

  • roft

    Real or Fake Text? Evaluation criteria for human-written and computer-generated text through the gamification of annotation. Published in EMNLP 2020.

  • OpenFilter

    This repository refers to the paper currently under review for the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, under the title "OpenFilter: A Framework to Democratize Research Access to Social Media AR Filters", by Piera Riccio, Bill Psomas, Francesco Galati, Francisco Escolano, Thomas Hofmann and Nuria Oliver.

  • monero-mrl-mj

    Experiments for Monero Research Lab (MRL)

  • AMAYARA-Lab

    The アマヤラ Lab project provides a ready-to-use Jupyter Lab environment to help out with Android malware analysis using YARA rules.

  • SaaSHub

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

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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).

Jupyter Notebook Research related posts

Index

What are some of the best open-source Research projects in Jupyter Notebook? This list will help you:

Project Stars
1 google-research 32,804
2 datasets 2,299
3 hyperlearn 1,510
4 awesome-data-centric-ai 300
5 image-crop-analysis 249
6 goodreads 228
7 benford_py 148
8 mnist1d 138
9 deepcourse 131
10 cobaltstrike-beacon-data 115
11 threat-research-and-intelligence 88
12 tnb-analysis 23
13 roft 11
14 OpenFilter 5
15 monero-mrl-mj 2
16 AMAYARA-Lab 1

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