Contribute To Open Source Data Science Projects

You can contribute to open source projects using GitHub and become a better data scientist by learning computer programming. Open Source Data Science Projects are everywhere and there is no better way to learn about data science. This course will help you learn how!

Are you looking to contribute to open source data science projects? There are hundreds of real life data science projects on GitHub and there is bound to be one that interests you. This 4-week tutorial will take you through basic to intermediate levels of analysis and machine learning in the R programming language and culminates into a complete end-to-end machine learning system that you can deploy to production.

 Google’s Caliban for Machine Learning

Let’s kick this list off with a project from the tech giant, Google. Often when building and developing data science projects, you may find it difficult to build a test environment that will show you your project in a real-life situation. You can’t predict all scenarios and make sure to cover all edge cases.

Google offers Caliban as a potential solution for that problem. Caliban is a testing tool that tracks your environmental properties during execution and allows you to reproduce specific running environments. Researchers and data engineers developed this tool at Google that performs this task on a daily basis.

Simplifying Machine Learning Papers – An Open-Source Project 

Most people find it extremely difficult to cope with the technicalities of machine learning when they begin their careers. Studying machine learning-related research papers is especially daunting as they contain terms and annotations that are extremely hard to understand for a beginner. An interesting project that is open-sourced on Github aims to solve just that. 

The project is basically a collection of machine learning related papers. It contains illustrations, annotations, and explanations of technical terminologies making it easier to understand the core concept. If you are a beginner, this is definitely a project you should check out. It will give you clarity on several key machine learning annotations that can help you in your journey ahead.

The project already has a collection of interesting and informative papers and is being updated regularly. Check out this object detection example which is one of the most interesting parts of the project.

PalmerPenguins

Next on our list is PalmerPenguins, a dataset that was only recently open-sourced. This dataset was built and developed to replace the very well-known and used Iris dataset. The reason behind Iris’s fame is its simplicity of use for beginners and also the wide variety of its possible applications.

PalmerPenguins offers an amazing dataset that you can use for data visualization and classification applications with the same ease as you would use Iris, but with much more options. One more great aspect of this dataset is that it offers art to teach data science concepts.

 Exploring NeoML

If you are someone who has an introductory knowledge of data science, this is an exciting project that you should definitely explore. Often, a great machine learning project idea fails to get executed owing to its high cost of development. NeoML tries to solve this problem.

NeoML is a machine learning framework that can help you build, train, and deploy machine learning models. In short, with NeoML, you no longer have to worry about huge investments and can instantly start building your own machine learning pipeline today. Many open-source project ideas like natural language processing, image preprocessing, data extraction from unstructured data, and computer vision can be deployed using NeoML.

Using NeoML to try out some of these interesting ideas will teach you a lot about machine learning and how it can be applied successfully. 

Caffe

Next up we have one of the promising frameworks for deep learning out there, Caffe. Caffe is a deep learning framework that was designed and built with speed, modularity, and expression as priorities. Caffe was originally developed by a team of researchers from the UC Berkeley AI lab and the vision and learning community.

After only one year of releasing Caffe as an open-source project, it was forked by more than 1000 researchers and developers around the world. It helped transform research topics and build new startups and industrial forces. The Caffe community is one of the welcoming, supportive open-source communities to join.

Face Recognition 

Face recognition is now a fully explored machine learning application found on almost every smartphone today. It is usually used as an encryption standard to unlock a user’s device. There’s a lot to learn from this open-sourced project that can benefit you if you are exploring machine learning. You can use this project to manipulate and recognize faces using simple Python programs or through the command line.

You can also try to make variations to this project idea and alter its purpose to solve some other interesting problem statements. One example could be of detecting a face mask like how it’s done here.

 Kornia

We’ll conclude our list with Kornia. Kornia is a supporting computer vision library for PyTorch. It includes various routines and differentiable that can be used to solve some generic computer vision problems. Kornia is built upon PyTorch and heavily depends on its efficiency and CPU power to compute complex functions.

Korina is more than just a package; it is a set of libraries that can be used together to train models and neural networks and perform image transformation, image filtering, and edge detection.

Regenerating A Target Picture 

This is one of the most interesting open-source projects which you can use to imitate a drawing process. This program needs a target image that can be replicated in great detail. You can also specify sampling masks if you need more brush-strokes at certain places in the image. This enables you to control every detail while replicating the target picture. 

To work on this project you will need the following python 3 libraries: 

a) opencv 3.4.1

b) numpy 1.16.2

c) matplotlib 3.0.3

d) Jupyter Notebook

If you are interested to learn about computer vision, this is one of the best open-source projects you can start exploring. It will give you a great idea of the fundamentals and prepare you to take on complex projects as well. 

Convert Images to 3D 

To build 3D models using 2D images was once a feat that could only be achieved through a deep understanding of design and hands-on experience with tools like Photoshop. However, due to the progress we have made in the field of computer vision, this can now be done using a few lines of code.

This is another interesting open-source project you can try out to understand more about computer vision. It takes a single RGB-D image as an input and converts each of its components to build a 3D photo. You can also try to read about a framework called PyTorch which has been extensively used in this example.

Apache Beam

Source: Google Cloud Platform

Apache Beam is an advanced unified programming open-source model launched in 2016. It derives its name  “Beam” which is from “Batch” + “Stream” from its functionalities for both batch and streaming the parallel processing pipelines for data. To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark, Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc. It even allows you to build a program that defines the data pipeline using open-source Beam SDKs (Software Development Kits) in any three programming languages: Java, Python, and Go.

Apache Beam has certain features that give an advantage to the user, the primary one being unified batch and streaming APIs with an increased level of abstraction and portability across runtimes. The only pitfalls are lesser transparency and control, the limited scope of performance improvement tricks compared to other Apache APIs, and open bugs.

Clickhouse

Source: Github

Clickhouse is a column-oriented database management system used for the online analytical processing of queries ( also known as OLAP). It allows the creation of tables and databases in runtime, loading data, and running queries without reconfiguring or restarting the server. With reduced disk IO, data locality, and compression, clickhouse is 100-1000x faster than traditional approaches. 

Some of its distinct features include data compression with specialized codecs for excellent performance, disk storage of data, parallel processing on multiple cores, distributed processing on various servers, SQL support, vector computation engine, real-time data updates, adaptive join algorithm, data replication, and data integrity support, role-based access control, etc. 

Companies like Yandex, CloudFare, Uber, eBay, Spotify have preferred Clickhouse owing to its performance, scalability, reliability, and security. On the contrary, the absence of developed transactions, lack of capacity to switch or delete inserted data with a high rate, low latency, and sparse index are the aspects that create a slight backlash for ClickHouse.

PULSE – Building High-Resolution Images

PULSE, which stands for Photo Upsampling via Latent Space Exploration aims to generate high-resolution images from low-resolution image inputs. It can also be used as a face de-pixelizer.

PULSE is thus a classic project in understanding computer vision. It is capable of producing extremely high-resolution images in a completely self-supervised fashion. Before you try out this project idea, explore how the fundamental concept of PULSE works. This will help you in better understanding its code.

Conclusion

If you want to contribute to open source data science projects, you need to make sure that the data is correct and the code is maintainable. This book will help you write well-factored R code and organize your R packages so that other people can easily read and modify them.

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