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Articles related to "install"


I made a mechanical keyboard with 3D-printed switches

  • This is basically the result of what I've been working on for the past 2 months, which has involved 3 iterations of testing machines, over 100 printed switches, and now finally a keyboard that I can type on.
  • If you want to learn more about the switches, I would recommend looking at some of my other posts tagged "keyboard", but in short: pressing on the stem at the top compresses the spiral spring (I copied the spring design from Riskable 3D Printing, he is also developing 3d-printable keyboard switches, but using magnet sensors instead of making mechanical switches himself).
  • There's not a lot to the case design really, it is just a shape to fit the switches into, with a bulge at the top to house the microcontroller, an exit hole for the cable, stabiliser mounts, and a cool-looking logo.

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Docker install and upgrade guide – CentOS and Ubuntu

  • Docker installation - requirements and quick commands cheet sheet!
  • Install and upgrade of Docker Engine is simple and straightforward process.
  • In this post I will cover all important information in one place.
  • Docker engine will set it by default.
  • If you plan to use Docker Enterprise with multiple nodes or Swarm - remember to set up time synchro between nodes - use NTP for that.
  • For installing Docker Enterprise Edition you have to set up repo on your OS after getting from Docker Hub Account Docker EE Repo Adress.
  • If you want that running containers won’t stop with docker daemon you can enable live restore feature.
  • After enabling it you can perform upgrade of docker engine without stopping running containers.
  • Upgrade on CentOS or Ubuntu - if we install docker engine from repo - can be performed with simple yum upgrade or apt-get upgrade commands.

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Nikola Can Only Blame Itself for Punishing Stock Meltdown

  • Nikola’s first-ever quarterly report as a public company was supposed to come and go without incident.
  • Nikola Motors (NASDAQ: NKLA) announced it had generated $36,000 in revenue during the quarter that ended in June.
  • Although nobody expected Nikola to generate any revenue, the figure still sparked chuckles, considering the company boasts a $14 billion valuation.
  • Per the quarterly report, the sole source of the revenues was the “provision of solar installation services to the Executive Chairman” and founder Trevor Milton.
  • Yep, the founder was the only client of Nikola’s solar installation business.
  • Even more curious is the fact that solar installation is not Nikola’s primary business.
  • Nikola’s rivals, including Rivian and Tesla (NASDAQ: TSLA), all started accepting reservations after showcasing working prototypes.
  • A little more transparency on Nikola’s part would go a long way toward assuring shareholders the company warrants its lofty valuation.

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Why Should You Learn Vim in 2020

  • The following command is 8j, letting Vim know that I want to go eight lines down.
  • Navigating like that is a typical example, but combining commands is where Vim shines when you get the hang of it.
  • When you use an IDE-like experience, you don’t need to tinker and configure stuff much.
  • A great way to do this is to install Vim mode in your favorite editor and start with simple commands as I showed you in the navigation part.
  • If you are still wondering whether to start learning Vim or not, I’d say give it a try.
  • You never know when you might need it, or you even fully switch to using Vim. At the end of the day, it’s the matter of finding the proper editor (tool) that makes you do what you do even better.

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PyCaret 2.0 is here — What’s New?

  • There’s no doubt that PyCaret is my favorite machine learning library.
  • It’s more developer-friendly than, let’s say, Scikit-Learn, and provides built-in functions for tackling major machine learning tasks with ease.
  • We’ll discuss the most useful new features next.
  • It’s a nice feature to have, as it shows decent speed training speed improvements for QDA instead of the Extra Trees Classifier algorithm while providing the same performance.
  • My previous articles on PyCaret showed how easy it is to make great-looking visualizations based on your model performance.
  • You can read more about MLFlow on this link, but in summary, it’s a nice GUI for managing machine learning lifecycle, and in this case, comparing models performance.
  • This was a rather quick article demonstrating the most useful new features of PyCaret 2.0, according to my current experience with the library.

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Robust Facial Landmarks for Occluded Angled Faces

  • Facial landmarks detection or facial keypoints detection has a lot of uses in computer vision like face alignment, drowsiness detection, Snapchat filters to name a few.
  • The most widely known model for this task is Dlib’s 68 keypoints landmark predictor which gives very good results in real-time.
  • It also gives 68 landmarks and it is a Tensorflow CNN trained on 5 datasets!
  • In the next two articles, the work is to extract the faces and apply facial landmarks on it to make it ready to train a CNN and store them as TFRecord files.
  • In the sixth article, a model is trained using Tensorflow.
  • So if speed is the main concern and occluded or angled faces not so much then Dlib might be better suited for you otherwise I feel that the Tensorflow model reigns supreme without compromising a lot on speed.

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PyCaret 2.0 is here — What’s New?

  • There’s no doubt that PyCaret is my favorite machine learning library.
  • It’s more developer-friendly than, let’s say, Scikit-Learn, and provides built-in functions for tackling major machine learning tasks with ease.
  • We’ll discuss the most useful new features next.
  • It’s a nice feature to have, as it shows decent speed training speed improvements for QDA instead of the Extra Trees Classifier algorithm while providing the same performance.
  • My previous articles on PyCaret showed how easy it is to make great-looking visualizations based on your model performance.
  • You can read more about MLFlow on this link, but in summary, it’s a nice GUI for managing machine learning lifecycle, and in this case, comparing models performance.
  • This was a rather quick article demonstrating the most useful new features of PyCaret 2.0, according to my current experience with the library.

save | comments | report | share on


Robust Facial Landmarks for Occluded Angled Faces

  • Facial landmarks detection or facial keypoints detection has a lot of uses in computer vision like face alignment, drowsiness detection, Snapchat filters to name a few.
  • The most widely known model for this task is Dlib’s 68 keypoints landmark predictor which gives very good results in real-time.
  • It also gives 68 landmarks and it is a Tensorflow CNN trained on 5 datasets!
  • In the next two articles, the work is to extract the faces and apply facial landmarks on it to make it ready to train a CNN and store them as TFRecord files.
  • In the sixth article, a model is trained using Tensorflow.
  • So if speed is the main concern and occluded or angled faces not so much then Dlib might be better suited for you otherwise I feel that the Tensorflow model reigns supreme without compromising a lot on speed.

save | comments | report | share on