Our Favourite Repositories
Machine-Learning-Processing includes many machine learning and deep learning examples with an opportunity of visualization. In this repository, one might find simple but educative examples.
- This repository integrates the concept of machine learning into a visualization-based programming languages
- The owner of this repository has a wonderful YouTube channel in which he does interesting stuff
- There is a ReadMe file includes additional resources in case of any interest
public-apis holds a collective list of free APIs for use in software and web development. There are currently 1427 APIs in the repository. In the repository, you can find a short description of what the API is about, and a link to its documentation.
- It is one of the best places to look if you need an API for your application.
- APIs are grouped into different categories, making it easier to navigate.
- You can come up with new ideas as you explore the categories.
With 198k stars and 24.7k forks, Awesome Python repository is one of the most popular and visited repos of GitHub. It gathers Python libraries, frameworks and resources for a wide range of fields such as cryptography, asynchronous programming, audio and image processing, geolocation and so on...
- This repository contains links to large number of repositories of libraries and frameworks
- It categorizes the resources which provide a better research experience
- In each of my visits, I discover new AI tools and libraries which contributes to my technical improvement
Jenkins is a highly influential automation server that helps developers to create their continuous integration and continuous delivery pipelines in software projects. This open-source project is vital for developers looking to automate their build, test, and deployment processes. It has more than 1800 plugins to customize the CI/CD operations.
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Because it has an extensive guidelines for contribution, creating issues, and pull requests. This is an open source project and widely used in the industry. This means that a beginner can contribute to the project while learning how it works.
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Pull request labels are really descriptive. Since this repository is really active and like every other enterprise grade application, it has problems. They manage the PR's really good. They chat channels to make it easier to communicate about the repository.
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It has a steep learning curve which may look hard to understand but when the developer dives into the application, it becomes easier to use.
You can checkout the Jenkins repository from the link: Jenkins
Pandas is a Python library offering fast, flexible data structures for intuitive relational or labeled data handling, aiming to be a key tool for Python-based data analysis. Its ambition extends to becoming the leading open-source data manipulation tool across all languages. It's progressing rapidly towards this goal.
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Widely used in data manipulation and Machine Learning, making it a top Python library.
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Extensive community support with ~150 labels and numerous issues for collaboration.
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Clear, detailed README for straightforward guidance.
Repo link: Pandas
This repo by Microsoft contains 18 Lessons teaching everything you need to know to start building Generative AI applications. Each lesson covers its own topic so start wherever you would like!
Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both Python and TypeScript when possible.
Each lesson also includes a "Keep Learning" section with additional learning tools.
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Not only gives general informations but also lets you learn by doing.
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Covers all the steps of Software Development Lifecycle for developing Generative AI Applications.
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Clear, detailed README for understanding what to expect from the lessons.
Repo link: microsoft/generative-ai-for-beginners