Programmers gain valuable real-world skills from algorithm competitions that can boost their job prospects—and contest ratings make it easier for hiring managers to find top talent. Explore competitive C++ with a programmer whose scores got them recruited by Google.
Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here’s how to amplify your data insights with comparison metrics, including the F-measure.
Unwanted AI bias is already a widespread problem. Machine learning models can replicate or exacerbate existing biases, often in ways that are not detected until release. So what can be done about it?
Automatically scaling container deployments in a microservices-based app architecture is downright luxurious…once it’s set up. But what’s the best way to tune an app’s orchestration parameters?
While machine learning sounds highly technical, an introduction to the statistical methods involved quickly brings it within reach. In this article, Toptal Freelance Software Engineer Vladyslav Millier explores basic supervised machine learning algorithms and scikit-learn, using them to predict survival rates for Titanic passengers.
Consistent Hashing is a distributed hashing scheme that operates independently of the number of servers or objects in a distributed hash table. It powers many high-traffic dynamic websites and web applications.
In this tutorial, Toptal Freelance Software Engineer Juan Pablo Carzolio will walk us through what it is and how hashing, distributed hashing and consistent hashing work.
Juan Pablo Carzolio
Image processing algorithms are often very resource intensive due to fact that they process pixels on an image one at a time and often requires multiple passes. Successive Mean Quantization Transform (SMQT) is one such resource intensive algorithm that can process images taken in low-light conditions and reveal details from dark regions of the image.
In this article, Toptal engineer Daniel Angel Munoz Trejo gives us some insight into how the SMQT algorithm works and walks us through a clever optimization technique to make the algorithm a viable option for handheld devices.
The Internet is becoming “smarter” every day. The video-sharing website that you frequently visit seems to know exactly what you will like, even before you have seen it. The online shopping cart holding your items almost magically figures out the one thing that you may have missed or intended to add before checking out. It’s as if these web services are reading your mind—or are they?
Turns out, predicting a user’s likes involves more math than magic. In this article we explore one of the many ways of building a recommendation engine that is both simple to implement and understand.
You hear a familiar song in the club or the restaurant. You listened to this song a thousand times long ago, and the sentimentality of the song really touches your heart. You desperately want to heart it tomorrow, but you can’t remember its name! Fortunately, in our amazing futuristic world, you have a phone with music recognition software installed, and you are saved.
But how does this really work? Shazam’s algorithm was revealed to world in 2003. In this article we’ll go over the fundamentals of that algorithm.
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