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Articles by Hucker Marius
RIP BERT: Google’s MUM is coming
Of course, MUM is a cleverly chosen marketing name to let the tech world easily communicate about the new Google algorithm (like there have been Pandas, Penguin & co.). Which algorithm(s) or machine learning systems are really behind the algorithm is not fully revealed. This is Google’s strategy to softly introduce their new astonishing search machine algorithm to the world, without letting too much light in the dark.
Uncovering How the Spotify Algorithm Works
In 2008 Spotify started changing the world around music by introducing music streaming. Since then music on CDs and DVDs has left all our lives and the music industry changed a whole lot. Nowadays, Spotify is the biggest player (with 365 Mio. users and 165 Mio. subscribers [1] ) in the music streaming market but has to maintain its position between American giants like Apple (Apple Music), Amazon (Amazon Music), and Google (YouTube Music).
A Brief Introduction to Azure Machine Learning Studio
Image by Author AAzure Machine Learning Studio is Microsoft’s central point of contact for Machine Learning Computation in the Azure cloud. It is the successor of the Microsoft Machine Learning Studio Classic, which will be retired in 2024. Over the years Microsoft expanded the number of features and possibilities within their Azure ML Studio and the progress is still ongoing. Microsoft tries to make the creation of and the work with algorithms and experiments as simple as possible.
HRNet explained: Human Pose Estimation, Sematic Segmentation and Object Detection
HRNet is a state-of-the-art algorithm in the field of semantic segmentation, facial landmark detection, and human pose estimation. It has shown superior results in semantic segmentation on datasets like PASCAL Context, LIP, Cityscapes, AFLW, COFW, and 300W. But first, let’s understand what the fields mean and what kind of algorithm hides behind HRNet. Semantic Segmentation Semantic Segmentation is used to categorizestructures of an image into certain classes.
How to Hack Almost Any Laptop
Forgot your password for Mac, Windows or Linux? Here are all Instructions to regain access! Source: Made by Andri fromPexels This is a very useful, but somehow also dangerous trick. Useful because you can still access your PC even if you forgot your password. Dangerous because anybody could steal a Mac or Windows PC and access it, even change the password. As always it depends on the person’s intention.
How to recognize and fix broken lines in table recognition
Repair holes and missing contours of your table using OpenCV / Python source: unsplash (Agê Barros). When documents are digitalized via scanning or via photo, the image quality can suffer from wrong settings or bad conditions. In the case of table recognition, this can lead to a broken table structure. Consequently, some lines might have light flaws or even holes and the table as a whole is not recognized as a coherent system.
Understanding the Basics of Digital Image Processing and Computer Vision using OpenCV
Fundamental Concepts of CV: Binarization, Thresholding, Grayscaling and more insights. This article is about the basic concepts behind a digital image, the processing of it, and hence, also the fundaments of CV. At the end you can find a simple code implementation with Python using OpenCV. What you’ll be able to answer, after you’ve read this article: How does a computer see an image? What are the typical types of images? What is binarization in the context of image processing? What is thresholding?
Multiclass Classification with Support Vector Machines (SVM), Dual Problem, Kernel Trick and Kernel Functions
Finally understand the concept behind SVM + Implementation in Python via scikit-learn source: unsplash (Bekky Bekks) Support Vector Machines (SVM) are not new but are still a powerful tool for classification due to their tendency not to overfit, but to perform well in many cases. Of course, there exist already other articles about SVM, but none of them fully explain the topics that will be covered here: What’s the mathematical concept behind the Support Vector Machine?
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