Activity of microsoft/generative-ai-for-beginners repository

Active 🚀

Active contributions

Why microsoft/generative-ai-for-beginners is active?

The result is based on ratio of number of commits and code additions from initial and final time ranges.

Initial time range – from 5 Jul, 2023 to 5 Oct, 2023

Final time range – from 5 Apr, 2024 to 5 Jul, 2024

From 2 to 4 commits per week

100%

From 152 to 1107 additions per week

628%

From 17 to 1959 deletions per week

11424%

Data calculated on 5 Jul, 2024

Bus factor

What is Bus factor?

It is basically a number of most active contributors responsible for 80% of contributions.

Bus factor tries to assess "What happens if a key member of the team is hit by a bus?". The more there are key members, the lower the risk.

The microsoft/generative-ai-for-beginners repository has a bus factor of 11.

Low risk, knowledge is well distributed among the team members

Bus factor was measured on 14 Aug 2024

Summary of microsoft/generative-ai-for-beginners

This GitHub repository, generative-ai-for-beginners, is published by Microsoft. It's designed to provide a comprehensive introduction to generative artificial intelligence for beginners. It offers a variety of instructional material, modules, tools, and resources for learning about generative AI concepts and techniques.

The contents of the repository are divided into several main directories:

instructions: This folder contains a markdown file providing a comprehensive guide to each of the modules.
actions: Contains all the code for the GitHub actions that accompany the project.
notebook: Here is where you can find Jupyter notebooks that guide users through various scenarios involving generative AI. Users will interact with code and learn about various procedures and techniques in generative AI through these notebooks.
solutions: Contains solution notebooks for users to compare and check with their own work when they complete the tasks in the notebooks.

The repository also contains a README.md file that provides an overview of the repository's content.

The final output of each exercise is a generative model capable of creating new content. This repository is useful for anyone interested in learning more about generative AI models, how they work, and how to use them.