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Top 23 Jupyter Notebook jupyternotebook Projects

Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, ScikitLearn, and related tools to effectively store, manipulate, and gain insight from data

ProbabilisticProgrammingandBayesianMethodsforHackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understandingfirst, mathematicssecond 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/ProbabilisticProgrammingandBayesianMethodsforHackers

InfluxDB
Collect and Analyze Billions of Data Points in Real Time. Manage all types of time series data in a single, purposebuilt database. Run at any scale in any environment in the cloud, onpremises, or at the edge.


homemademachinelearning
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

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.)

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  20231004 
TheCompleteFAANGPreparation
This repository contains all the DSA (DataStructures, 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.

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.

amazonsagemakerexamples
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/amazonsagemakerexamples/blob/main/introduction_to_amazon_algorithms/jumpstartfoundationmodels/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb




evidently
Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
Project mention: [P] Free opensource ML observability course: starts October 16 🚀  /r/MachineLearning  20231015Hi everyone, I’m one of the creators of Evidently, an opensource (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.

Project mention: Kali Linux 2023.1 introduces 'Purple' distro for defensive security  /r/netsec  20230314
Utilizing that api and juniper notebooks is exactly why Hunting Elk is the way it from my understanding.

I really like the simplicity of this framework, and they hit on a lot of common problems found in other agentbased 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 nonengineering resources.
This example was what caught my eye: https://github.com/microsoft/FLAML/blob/main/notebook/autoge...

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  20230106
Also check out: https://github.com/mltooling/mlworkspace, it a nice open source project with lots of packages ready to use.

CFDPython
A sequence of Jupyter notebooks featuring the "12 Steps to NavierStokes" 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.

MLfoundations
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 selfstudy 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/MLfoundations

awesomenotebooks
A catalog of ready to use data & AI Notebook templates, organized by tools to jumpstart your projects and data products in minutes.


Found a package na from GitHub that worked on my Macbook. Thanks everyone!




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Jupyter Notebook jupyternotebook related posts
 [P] Free opensource ML observability course: starts October 16 🚀
 A small set of Python functions to draw pretty maps from OpenStreetMap data
 Free Opensource ML observability course
 Show HN: OpenSource 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

A note from our sponsor  #<SponsorshipServiceOld:0x00007f0f9b0ace98>
www.saashub.com  9 Dec 2023
Index
What are some of the best opensource jupyternotebook projects in Jupyter Notebook? This list will help you:
Project  Stars  

1  PythonDataScienceHandbook  40,259 
2  ProbabilisticProgrammingandBayesianMethodsforHackers  26,034 
3  handsonml  25,059 
4  homemademachinelearning  22,021 
5  notebook  10,739 
6  prettymaps  10,537 
7  TheCompleteFAANGPreparation  9,640 
8  amazonsagemakerexamples  9,078 
9  NYUDLSP20  6,574 
10  py  6,426 
11  lucid  4,581 
12  evidently  4,159 
13  HELK  3,618 
14  FLAML  3,424 
15  MLWorkspace  3,217 
16  CFDPython  3,040 
17  MLfoundations  2,584 
18  awesomenotebooks  2,174 
19  dlcolabnotebooks  1,641 
20  IRkernel  1,600 
21  machinelearningassetmanagement  1,591 
22  osmnxexamples  1,365 
23  fastprogress  1,058 