GitHub allows developers from all over the world to collaborate. Open-source solutions, such as GitHub, enable potential developers to participate and share their knowledge for the benefit of the worldwide community.

Over 73 million programmers and developers utilise GitHub to host and share code in a cooperative and collaborative environment. It provides access control, version control, and continuous integration for every project and is the most popular source code host on the planet, with over 28 million public repositories. 

We have developed a list of the best repositories for learning data science from among them.

TensorFlow

TensorFlow is an open-source machine learning and artificial intelligence framework created by Google Brain Team. The GitHub repository provides a variety of materials for learning and improving TensorFlow and Machine learning skills.

It also delivers cutting-edge Machine Learning models in computer vision, NLP, and recommendation systems. As a result, they are highly optimised and efficient at work at hand, allowing them to employ them immediately and produce exact results on their datasets.

TensorFlow Lite

TensorFlow Lite is a product-ready open-source deep learning framework that can turn a TensorFlow pre-trained model into a bespoke model that can then be optimised for speed or storage. The concept can be deployed on lightweight edge devices such as Android or iOS phones, Linux-based devices such as the Raspberry Pi, and even microcontrollers. 

The particular model is also installed on the edge device, following which inferences are made on the device, ensuring data piracy safety concerns. 

500 AI-ML projects

One of the most critical aspects of learning any field, be it data science, AI, or any other, is to gain practical experience. In addition, most persons who study or pursue their interests in this discipline have the potential to produce data science initiatives. As a result, this repository offers one of the most basic listings, encompassing over 500 projects on machine learning, NLP, AI, and code.

TensorFlow Federated

TFF, or TensorFlow Federated, is an open-source ML framework created to help open up research and experimentation surrounding Federated Learning. The library will provide scholars with beginning points and comprehensive examples for study work in several fields covered by federated learning. 

Federated Learning is a strategy in machine learning that produces a single robust ML model without actually sharing the data, hence maintaining data security and distributing data rights fairly. Federated learning, for example, has been used to train prediction models for mobile keyboards without transmitting confidential typing data to servers. 

Protocol Buffers

Protocol Buffers, or protobuf, is a toolkit developed by Google for serialising structured data. Protobuf is language-neutral, platform-neutral, and extensible, and it is frequently used when defining communication protocols or storing data. The protocol compiler is written in C++ and presently supports Java, Python, Objective-C, and C++ code generation. 

TensorFlow Rust

The TensorFlow toolkit can help developers run Rust in production on micro-devices while deploying neural networks at periphery devices as Rust gains popularity. However, the toolkit comes with the disclaimer that the project is still in development, and a stable API cannot be guaranteed. Nevertheless, the combination of Rust and TensorFlow is potent due to the ease with which custom models can be designed and trained using the available bindings.

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