Explore code and developers on GitHub today, Jan 19.Here's what we found based on your interests...
HivemindThis year’s Game Off wrapped up with over 500 submissions. Check out the game called Hivemind - one of this year’s top entries.
ImgbotImgbot is a friendly robot that optimizes your images and saves you time. Optimized images mean smaller file sizes without sacrificing quality. Shortly after installing Imgbot, you will receive a pull request with all of your images optimized. Just merge the pull request and you’re done! As you work on your project, Imgbot works alongside you to keep your images optimized. Imgbot uses lossless compression by default.
Doorframe OZThis year’s Game Off wrapped up with over 500 submissions. Check out the game called Doorframe OZ - one of this year’s top entries.
Code for Data Engineer Zoomcamp course
Instant neural graphics primitives: lightning fast NeRF and more
Adds SciHub links to popular publisher websites
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Tuesday, January 18, 2022
GitHub Explore today Jan 19
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The best stovetop espresso makers (aka, Moka pots)
Hey coffee lover,
There's something deeply satisfying about a rich, aromatic cup of dark roast coffee.
Especially when it's made in a Moka pot.
You can have it any way you want - Americano, latte, cappuccino, or even poured over a scoop of vanilla ice cream for a quick affogato. 😋
The possibilities are endless.
But it all starts with specialty-grade, freshly roasted coffee beans. And the Moka pot itself, of course.
When it comes to getting one, you might gravitate toward the iconic Bialetti Moka express.
But it's made of aluminum, and you won't be able to use it on an induction stovetop.
It just goes to show that not all Moka pots are created equal.
But you should still pick up a high-quality brewer if you don't want to end up drinking mouth-puckeringly bitter coffee. So what options do you have?
Well, just because you asked...
Here's a list of the best stovetop espresso makers
Deb
Medium roast lover (& community manager)
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Defect Classification and Detection in Semiconductors : A new problem domain
Hello trend,
This is Satya Mallick from LearnOpenCV.com.
Today's post is the third prize winner of the recently concluded LearnOpenCV Blog Olympics.
Modern integrated circuits pack more than 100 million transistors in each square millimeter!
When the dimensions are so incredibly tiny, there is little margin for error. Without extensive inspection and metrology (measurement) runs, the process technology would have an unacceptable yield (i.e., most of the chips produced will be unusable.).
Regular readers of this newsletter know that Electron Microscopes are used when we need to resolve fine microscopic details because the wavelength of electrons is much shorter than the wavelength of photons. Image taken using Scanning Electron Microscopes (SEM) are very popular for defect verification and classification in the semiconductor industry.
Let's dive into today's post to understand a specific inspection problem in this domain and how the authors solved it using Deep Learning in the real world. You will also learn when the problems get tricky, it is a good idea to use an ensemble (collection) of many models to obtain higher accuracy.
While the authors have shared all the details of their implementation, they cannot share the actual code due to IP and confidentiality reasons.
As you can see in the post, the problems in this domain are cast as a classification, detection, or segmentation problem.
You'd be surprised how many applications you can develop in computer vision by having a good knowledge of just these three categories of problems.
Second, you will notice that when we have enough data, Deep Learning is the way to solve classification, detection, and segmentation problems.
Our course Deep Learning with PyTorch covers all three categories, and builds a strong foundation by teaching various concepts using practical examples. The course is designed to help a motivated beginner develop expert level skills in deep learning in about four months.
Want to solve AI problems in your domain?
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Cheers!
Satya
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HydraNets and the Future of Deep Learning
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