This is a great time to break through into this blooming field.
So make sure you check out the below two computer vision projects on GitHub to add to your portfolio.
And if you’re new to the world of computer vision, I suggest taking the below comprehensive course: Computer Vision using Deep Learning 2.
0 Tiler – Build Images with Images The ability to work with image data is being sought after quite a lot in the industry.
It’s not really a surprise, is it?.The number of images being uploaded and published these days is unprecedented.
And this pace will only increase in the next few years.
Tiler is a really awesome tool that helps us create an image using all kinds of smaller images (tiles to be precise).
As this repository says, “An image can be built out of circles, lines, waves, cross stitches, legos, Minecraft blocks, paper clips, letters, … The possibilities are endless!”.
You can check out some illustrated examples in the GitHub repository.
Here’s one to whet your appetite: So, go ahead and build your own images using other smaller images!.And if you’re new to the world of images for machines, here are three beginner-friendly articles for you: 3 Beginner-Friendly Techniques to Extract Features from Image Data using Python 9 Powerful Tips and Tricks for Working with Image Data using skimage in Python Feature Engineering for Images: A Valuable Introduction to the HOG Feature Descriptor DeepPrivacy – An Impressive Anonymization Technique for Images Privacy is in short supply in today’s digital world.
Every move we make and every touch of the screen is recorded, stored, analyzed and used to serve customized ads and offers (and many other things).
One of the major downsides of this lack of privacy has been the manipulation of images.
I’m sure you must have heard of DeepFakes by now.
For the uninitiated, it was the ability to manipulate a person’s expressions and facial muscles using just a few images.
It’s still a problem as the algorithm behind the concept, called Generative Adversarial Networks (GANs), has continued to evolve.
That’s why I really like DeepPrivacy – a fully automatic anonymization technique for images.
The GAN model behind DeepPrivacy never sees any privacy-sensitive information.
It generates the image(s) considering the original pose of the person and the image background.
DeepPrivacy uses Mask R-CNN to generate information about the face.
You can read the full research paper behind DeepPrivacy here.
And below are a couple of in-depth articles to help you get acquainted with GANs: Introductory Guide to Generative Adversarial Networks (GANs) and their promise!.Top 5 Interesting Applications of GANs for Every Machine Learning Enthusiast Other Useful Data Science Projects TubeMQ – Storing and Transmitting Big Data (Tencent) I’ve always been fascinated with how the top tech behemoths store and extract their data.
What does it feel like when your data operations scale up 10000x?.This kind of information isn’t usually made fully public.
That’s why we should be grateful to Tencent for open sourcing their distributed messaging queue (MQ) system called TubeMQ.
It’s been in use since 2013 so that’s almost seven years of data operations available to us!.TubeMQ focuses “on high-performance storage and transmission of massive data in big data scenarios”.
The user guide provides a step-by-step explanation of how to leverage TubeMQ for your organization.
DeepCTR – Torch Ever worked on a click-through rate (CTR) problem?.It’s intriguing and complex at the same time and it definitely takes a lot to unravel it.
DeepCTR is an easy-to-use package of deep learning-based CTR models.
It comes with multiple component layers that we can use to build our custom models.
You can use any model you want with model.
fit() and model.
I can see the sklearn fans smiling!.The original DeepCTR project was in TensorFlow.
Now TF is great but it isn’t to everyone’s taste.
And that’s how this DeepCTR-Torch repository was born.
It provides the entire original DeepCTR code in PyTorch.
Install it right now via pip: pip install -U deepctr-torch If you’re entirely new to click-through rate prediction, I suggest going through the below guide: A Comprehensive Guide to Digital Marketing and Analytics End Notes I fully expect to see more NLP projects filling up these monthly articles.
I started this series back in January 2018 and I’m amazed at where we are right now in all aspects of data science, especially NLP.
In comparison, progress in computer vision has stalled a little bit but that’s only because we’ve crossed a lot of obstacles to get to the current state.
I’m sure we’re one or two major developments away from opening the floodgates.
Are there any projects you feel I should include in this article?.Or did you find any of the above projects useful in your work?.I would love to hear from you in the comments section below.
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