5 Cool Projects you can work with to improve your Data Science Skills

There has been a lot of activity among fresher’s recently, especially in the Data Science community, where they are trying to make the best out of these hard times by enrolling in courses one after the other. It shows us how much they love the profession and want to improve their skills to get better opportunities.

I hope to see this trend of learning and keeping themselves up-to-date continue after the lockdown period.

Going back to enrolling for courses, reading theory, practicing on some very well known datasets like visualizing Iris species, predicting housing prices or pneumonia infected children, etc., can be very useful, but does it make you unique? How will you stand out if you are filling the resume with the same kind of projects which everyone has already done? What can you do so that you use your skills and apply them to real-world problems? How can you contribute to the Data Science community?

In today’s post, I will be introducing you to a few Open Source projects where you can learn, contribute, and, of course, stand out from the rest! Read on to find out what they are.

The Algorithms/Python

In Python’s repository alone, there are 472 contributors and around 500 Algorithms explained with problems and solutions! There are 22,000+ forks and people are still contributing to this repository. This is one of the best places to start learning, connecting with the community, as well as contributing to the community! Please check out the links provided below to read about them and start right away!

GitHub | Website | List of Algorithms/Python

Jukebox

Recently, they’ve open-sourced what they call as Jukebox. If you’re a music fan, you can intuitively understand that this is a neural network model that can generate music with singing in the raw audio domain.

This model was trained on a dataset of 1.2 million songs (600,000 of which are in English), paired with corresponding lyrics and metadata from LyricWiki. They also trained this on a 32-bit, 44.1 kHz raw audio, and performed data augmentation by randomly downmixing the right and left channels to produce mono audio.

If you want to play with Jukebox, all you’ve to do is provide a genre, an artist, and lyrics as input, and the neural network does the job for you in creating new music that takes the sample from the given input. The range of music Jukebox can generate is staggering!

Check out the below video, which contains the music and lyrics created by this artificially intelligent musician!

If you’re excited about testing this for yourself, go to the links provided below to read more about it and try it on Google Colab!

Code | Blog | Paper | Google Colab | Explorer (test)

BlenderBot

See the trend of advancement in Conversational AI Bot on Facebook!

Read the above conversation between a human(left) and BlenderBot(right). Noticed the ending? Shocking results.

They achieved this milestone through a new chatbot recipe that includes improved decoding techniques, a novel blending of skills, and a model with 9.4 billion parameters, which is 3.6x more than the largest existing system. Watch the video to see how BlenderBot sounds just like a human!

Check out the below links for more information on how you can play with it!

Code | Blog | Paper

Portrait to Cartoon (Photo to Cartoon)

In this project, you’ll find that a photo that contains a face is immediately turned into a cartoon. They used the Generative Adversarial Network method to realize the mapping of pictures to comics.

The results of CycleGAN, a classic unpaired image translation method, often have obvious artifacts and are unstable. Recently, Kim et al. propose a novel normalization function (AdaLIN) and an attention module in the paper “U-GAT-IT” and achieve excellent selfie2anime results.

You can also train your images following the instructions given in their repo. They also provided an official blog to test the images.

Original Image (left) vs. Transformed Image into Anime (right)

Code | Blog

3D Photography using Context-aware Layered Depth Inpainting

It is a multi-layer representation that contains hallucinated colour and depth structures in regions occluded in the original view. They used a Layered Depth Image with explicit pixel connectivity as underlying representation and presented a learning-based inpainting model that iteratively synthesizes new local colour-and-depth content into the occluded region in a spatial context-aware manner.

You’ll get stunning results, as shown in the below video. It also looks realistic when you zoom in! They even provide a Google Colab link for you to test on immediately. Check out the links provided.

Visualizing history in 3D:

Zoom in 3D:

Code | Blog | Paper | Google Colab


These projects can be of great help to build your resume and impress your recruiters. Not only that, you’d be staying up to date with what’s happening in the world of AI and Data Science. I hope you found these projects interesting, let us know which one you are interested and would be willing to try in the comments section. And as always, feel free to reach out to us with any questions or concerns and we’d love to help you out wherever we can!

What are you waiting for?

If you are interested in learning more about what we do at FIO Labs or have some questions about this page, feel free to send us a message to [email protected] — we’d love to hear from you.

How to reach FIO Labs:

Leave a Reply

Your email address will not be published. Required fields are marked *

Begin typing your search term above and press enter to search. Press ESC to cancel.

Back To Top