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Top 23 Jupyter Notebook jupyter-notebook Projects
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Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data
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Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Also this is quite nice practical introduction which might help with finding answers to your questions: https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
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InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purpose-built database. Run at any scale in any environment in the cloud, on-premises, or at the edge.
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homemade-machine-learning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
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For folks asking what the Notebook UX offers that the Lab does not, this github thread may be enlightening: https://github.com/jupyter/notebook/issues/6210
(TLDR: some novice users in educational settings find the lab environment overwhelming.)
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prettymaps
A small set of Python functions to draw pretty maps from OpenStreetMap data. Based on osmnx, matplotlib and shapely libraries.
Project mention: A small set of Python functions to draw pretty maps from OpenStreetMap data | news.ycombinator.com | 2023-10-04 -
The-Complete-FAANG-Preparation
This repository contains all the DSA (Data-Structures, Algorithms, 450 DSA by Love Babbar Bhaiya, FAANG Questions), Technical Subjects (OS + DBMS + SQL + CN + OOPs) Theory+Questions, FAANG Interview questions, and Miscellaneous Stuff (Programming MCQs, Puzzles, Aptitude, Reasoning). The Programming languages used for demonstration are C++, Python, and Java.
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Onboard AI
Learn any GitHub repo in 59 seconds. Onboard AI learns any GitHub repo in minutes and lets you chat with it to locate functionality, understand different parts, and generate new code. Use it for free at www.getonboard.dev.
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amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb
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evidently
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Project mention: [P] Free open-source ML observability course: starts October 16 🚀 | /r/MachineLearning | 2023-10-15Hi everyone, I’m one of the creators of Evidently, an open-source (Apache 2.0) tool for production ML monitoring. We’ve just launched a free open course on ML observability that I wanted to share with the community.
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Project mention: Kali Linux 2023.1 introduces 'Purple' distro for defensive security | /r/netsec | 2023-03-14
Utilizing that api and juniper notebooks is exactly why Hunting Elk is the way it from my understanding.
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I really like the simplicity of this framework, and they hit on a lot of common problems found in other agent-based frameworks. Most intrigued by the RAG improvements.
Seems like Microsoft was frustrated with the pace of movement in this space and the shitty results of agents (which admittedly kept my interest turned away from agents for the last few months). I'm interested again because it makes practical sense, and from looking at the example notebooks, seems fairly easy to integrate into existing applications.
Maybe this is the 'low code' approach that might actually work, and bridge together engineering and non-engineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...
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Project mention: [D] I recently quit my job to start a ML company. Would really appreciate feedback on what we're working on. | /r/MachineLearning | 2023-01-06
Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.
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CFDPython
A sequence of Jupyter notebooks featuring the "12 Steps to Navier-Stokes" http://lorenabarba.com/
Is 12 steps to Navier Stokes a good start? I have done all the modules, wrote all the code by myself (except for the plotting part which I had literally no experience in) and I am trying to solve some random problems in the J P Holman heat transfer book. Then I am thinking of going through the Application part of Anderson CFD.
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ML-foundations
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
As others have said, you won't need calculus immediately, but it's important that you make a good attempt at learning up to Calc3. I also didn't have a math heavy undergrad so it took a lot of self-study for me, but it's possible. Simulation has a great math boot camp at the beginning to review everything but you'll want to be prepped with Calc before that because that class is all calculus based probability. Some other good resources are the 3Blue1Brown videos on YouTube. They have a great series for both calc & linear algebra to talk through all the intuition with visuals. I also really like John Krohns series because you code through the math which is very applicable for us in this program. I only did his linear Algebra, but he has a whole series with Calc and probability, too. https://github.com/jonkrohn/ML-foundations
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awesome-notebooks
A catalog of ready to use data & AI Notebook templates, organized by tools to jumpstart your projects and data products in minutes.
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Found a package na from GitHub that worked on my Macbook. Thanks everyone!
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SaaSHub
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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).
The latest post mention was on 2023-10-15.
Jupyter Notebook jupyter-notebook related posts
- [P] Free open-source ML observability course: starts October 16 🚀
- A small set of Python functions to draw pretty maps from OpenStreetMap data
- Free Open-source ML observability course
- Show HN: Open-Source Web App with User Interface for AutoML on Tabular Data
- Japan loses No. 1 spot in powerful passport rankings
- Riddle me this: "Huku ni wapi?"
- BGV fully homomorphic encryption scheme, a toy implementation in Python
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A note from our sponsor - #<SponsorshipServiceOld:0x00007f0f9b0ace98>
www.saashub.com | 9 Dec 2023
Index
What are some of the best open-source jupyter-notebook projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | PythonDataScienceHandbook | 40,259 |
| 2 | Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | 26,034 |
| 3 | handson-ml | 25,059 |
| 4 | homemade-machine-learning | 22,021 |
| 5 | notebook | 10,739 |
| 6 | prettymaps | 10,537 |
| 7 | The-Complete-FAANG-Preparation | 9,640 |
| 8 | amazon-sagemaker-examples | 9,078 |
| 9 | NYU-DLSP20 | 6,574 |
| 10 | py | 6,426 |
| 11 | lucid | 4,581 |
| 12 | evidently | 4,159 |
| 13 | HELK | 3,618 |
| 14 | FLAML | 3,424 |
| 15 | ML-Workspace | 3,217 |
| 16 | CFDPython | 3,040 |
| 17 | ML-foundations | 2,584 |
| 18 | awesome-notebooks | 2,174 |
| 19 | dl-colab-notebooks | 1,641 |
| 20 | IRkernel | 1,600 |
| 21 | machine-learning-asset-management | 1,591 |
| 22 | osmnx-examples | 1,365 |
| 23 | fastprogress | 1,058 |
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