Copilot Explains - Error troubleshooting in Jupyter Notebooks
Data scientists and AI engineers love to work with Jupyter Notebooks because they make so much easier to look at the result of each and every data exploration step or data modeling experiment and take decisions accordingly. However, Jupyter notebooks are not immune to errors and sometimes understanding error messages - in particular if you aren’t a native English speaker or you are a beginner - and troubleshooting code might be painful and time consuming.
Why I am getting the eroor ''Illegal instructions (core dumped)'' when I try to open jupyter
It was all going well but after installing streamlit and running its app I got this message. I tried to google and tried to solve problem which contains a little line of modification in bash shell which I did. After restarting ubuntu, the jupyter notebook stopped working with the same message My OS Ubuntu with KDE desktopUC Berkeley and Microsoft Learn - Foundations of Data Science Data8
The UC Bekerely Computing, Data Science, and Society (DSEP) division is proud to announce the new Learning Path Foundations of Data Science on Microsoft Learn based on the Computational and Inferential Thinking textbook authored by Berkeley faculty members Ani Adhikari, John DeNero, and David Wagner. The Foundations of Data Science course serves as an introductory data science course that teaches data science from the ground up, without any prerequisite knowledge in programming or statistics.
Build, train, and evaluate an object detection model using ComputerVision Recipes
In this article, we will show how to build, train, and evaluate an object detection model in just a few lines of code using the open-source Computer Vision Recipes repository. The repository supports various scenarios, including classification, retrieval, segmentation, detection, tracking, and action recognition.Jupyter Experiences on Microsoft Azure
The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much moreJupyter Notebook Workflow - DevOps for Data Science
DevOps is popular technology best practice and it is often used in combination with Python. The center of the universe for DevOps is the build server. The build server enables automation. This automation includes linting, testing, reporting, building and deploying code. This process is called continuous delivery.
