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Top 23 Jupyter Notebook AI Projects
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generative-ai-for-beginners
21 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
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InfluxDB
InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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Project mention: Implement a ChatGPT-like LLM in PyTorch from scratch, step by step | news.ycombinator.com | 2025-04-24
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Project mention: The Top 9️⃣ Repositories to learn Python programming + Resources (Extra) 🤯 | dev.to | 2024-11-06
⭐️ AI For Beginners on GitHub.
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I tested Phi-4 with a Japanese functional test suite and it scored much better than prior Phis (and comparable to much larger models, basically in the top tier atm). [1]
The one red-flag w/ Phi-4 is that it's IFEval score is relatively low. IFEval has specific types of constraints (forbidden words, capitalization, etc) it tests for [2] but its one area especially worth keeping an eye out for those testing Phi-4 for themselves...
[1] https://docs.google.com/spreadsheets/u/3/d/18n--cIaVt49kOh-G...
[2] https://github.com/google-research/google-research/blob/mast...
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h4cker
This repository is primarily maintained by Omar Santos (@santosomar) and includes thousands of resources related to ethical hacking, bug bounties, digital forensics and incident response (DFIR), artificial intelligence security, vulnerability research, exploit development, reverse engineering, and more.
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llama-cookbook
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Meta Platforms has unveiled its latest suite of large language models (LLMs) under the Llama 4 series, marking a significant advancement in artificial intelligence technology. The Llama 4 collection introduces two primary models in April 2025: Llama 4 Scout and Llama 4 Maverick. These models are designed to process and translate various data formats, including text, video, images, and audio, showcasing their multimodal capabilities. Additionally, Meta has previewed Llama 4 Behemoth, an upcoming model touted as one of the most powerful LLMs to date, intended to assist in training future models.
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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dopamine
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
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Dreambooth-Stable-Diffusion
Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focused on training faces, objects, and styles. (by JoePenna)
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mito
Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet
3. Tables that translate as Pandas dataframes. We support at most one table per sheet, at the tables must be contigious. If the formulas in a column are consistent, then we will try and translate this as a single pandas statement.
We do not support: pivot tables or complex formulas. When we fail to translate these, we generate TODO statements. We also don’t support graphs or macros - and you won’t see these reflected in the output at all currently.
*Why we built this:*
We did YCS20 and built an open source tool called [Mito](https://trymito.io). It’s been a good journey since then - we’ve scaled revenue and to over [2k Github stars](https://github.com/mito-ds/mito). But fundamentally, Mito is a tool that’s useful for Excel users who wanted to start writing Python code more effectively.
We wanted to take another stab at the Excel -> Python pain point that was more developer focused - that helped developers that have to translate Excel files into Python do this much more quickly. Hence, Pyoneer!
I’ll be in the comments today if you’ve got feedback, criticism, questions, or comments.
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Project mention: Show HN: Cognee – Turn RAG and GraphRAG into custom dynamic semantic memory | news.ycombinator.com | 2025-02-12
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Publishing this around the time of Google Cloud NEXT 2025, so scan for exciting news from that event this week (and say HI if you run into me there)! This post dives into one of the newer capabilities of the Gemini 2.0 Flash model, continuing the conversation from where we left off after looking at its audio generation capabilities. By the end of this post, you'll know how to use the Gemini API (via Google AI) for (simple) image generation.
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machine-learning-experiments
🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
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awesome-ai-ml-dl
Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.
Project mention: Show HN: Llama 3.2 Interpretability with Sparse Autoencoders | news.ycombinator.com | 2024-11-21From https://news.ycombinator.com/item?id=34619013 :
> /? awesome "explainable ai" https://www.google.com/search?q=awesome+%22explainable+ai%22
- (Many other great resources)
- https://github.com/neomatrix369/awesome-ai-ml-dl/blob/master... :
> Post model-creation analysis, ML interpretation/explainability
> /? awesome "explainable ai" "XAI"
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imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Project mention: PiML: Python Interpretable Machine Learning Toolbox | news.ycombinator.com | 2024-11-05[2] https://github.com/csinva/imodels/issues/129
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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 AI discussion
Jupyter Notebook AI related posts
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Generating images with Gemini 2.0 Flash from Google
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Meta Llama 4 Model Series Full Analysis
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Meta: Llama4
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Run GenAI Models Locally with Docker Model Runner
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Generative-AI-for-beginners: 21 Lessons
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AI APIs in 2025
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Guide to LLM Training, Fine-Tuning, and RAG
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A note from our sponsor - CodeRabbit
coderabbit.ai | 25 Apr 2025
Index
What are some of the best open-source AI projects in Jupyter Notebook? This list will help you:
| # | Project | Stars |
|---|---|---|
| 1 | generative-ai-for-beginners | 78,956 |
| 2 | LLMs-from-scratch | 44,756 |
| 3 | AI-For-Beginners | 37,185 |
| 4 | google-research | 35,397 |
| 5 | learnopencv | 21,821 |
| 6 | h4cker | 20,633 |
| 7 | llama-cookbook | 17,102 |
| 8 | StableLM | 15,830 |
| 9 | stable-diffusion-webui-colab | 15,824 |
| 10 | ML-Papers-of-the-Week | 11,112 |
| 11 | dopamine | 10,710 |
| 12 | sweep | 7,549 |
| 13 | nlpaug | 4,551 |
| 14 | ArtLine | 3,614 |
| 15 | Dreambooth-Stable-Diffusion | 3,213 |
| 16 | examples | 2,870 |
| 17 | clip-retrieval | 2,535 |
| 18 | mito | 2,447 |
| 19 | cognee | 1,985 |
| 20 | generative-ai-docs | 1,975 |
| 21 | machine-learning-experiments | 1,701 |
| 22 | awesome-ai-ml-dl | 1,527 |
| 23 | imodels | 1,443 |
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