The wave of AI and machine learning is happening just as the dominance of mobile is becoming set in stone. As mobile devices become more ubiquitous and powerful, a lot of the machine learning tasks we think of as requiring months of high-powered compute time will be able to happen right on your phone.
This post will outline why edge devices are increasingly important, and how machine learning works with them.
Continue reading “Machine Learning models on the edge: mobile and IoT”
Hair simulation is a great way to simulate different but realistic hairstyles. The Hair Simulation effect lets you know which hairstyle suits your face, and it’s quite a lot of fun. We can simulate short, long, and different color hairstyles in Lens Studio.
In Snapchat’s Lens Studio, we will use the Hair Simulation template to try and simulate some beautiful hairstyles.
Continue reading “Add Hair Simulation Effect Using Lens Studio”
As an ML engineer or data scientist, sometimes you inevitably find yourself in a situation where you have hundreds of records for one class label and thousands of records for another class label.
Upon training your model you obtain an accuracy above 90%. You then realize that the model is predicting everything as if it’s in the class with the majority of records.
Excellent examples of this are fraud detection problems and churn prediction problems, where the majority of the records are in the negative class. What do you do in such a scenario? That will be the focus of this post.
Continue reading “Dealing with Imbalanced Data in Machine Learning”
Emilio Vegas, known online as Emiliusvgs, is reshaping AR. The Peruvian creator specializes in AR effects and concepts and has been steadily growing his following on Instagram and YouTube.
Testing products, tweaking Snapchat lenses, and vlogging, Vegas keeps busy with his so-called “Metaverse” project. You can visit his YouTube for more tutorials and product reviews, but first, read on to learn more about how Vegas got his start.
There was no shortage of excellent computer vision research presented during the recently-concluded 2020 CVPR conference. In this article, we’ll take a focused look at a couple of those that touched or have implications for mobile or edge-related tasks.
While not all of these papers connect directly to mobile-first applications, their implications for mobile ML are significant. They push forward ML tasks commonly performed on mobile and edge devices, so their advancement is crucial in pushing the industry forward.
Continue reading “CVPR 2020: Research with Mobile ML Implications”
Oftentimes, sci-fi movie storylines start with a robot that realizes its role, and the roles of other similar robots in a colony.
The plot then progresses as a form of collaboration between these robots, as they deviate from the human-designed objectives, ignoring human safety to some evil control conspiracies.
Continue reading “Developing AI consciousness in connected autonomous vehicles (CAVs)”
Welcome back to my series on building image effects for Android using OpenCV. In this tutorial, which is part 5 of the series, we’re going to build an image pattern—filling a large image with a pattern created by repeating a small image.
The GitHub project for the series is available on this page. The project of this tutorial is available inside the Part 5 folder under the project.
Continue reading “Image Effects for Android using OpenCV: Image Patterns”
Char Stiles is a regular at Algorave events across the East Coast of the United States. Based in Brooklyn, NY, the programmer and artist keeps busy with livecoding events, teaching workshops, and collaborating with Snapchat and artists of all kinds.
Interested in learning the math behind the code as well as exploring the philosophy of machine learning, Stiles is making their own path. I reached out to the creator after Lens Fest 2020 to learn more.
Federated learning is a new way of training a machine learning using distributed data that is not centralized in a server. It works by training a generic (shared) model with a given user’s private data, without having direct access to such data.
For a deeper dive into how this works, I’d encourage you to check out my previous blog post, which provides a high-level overview, as well as an in depth look at Google’s research.
If you’ve ever considered working with the new Core ML framework in iOS, you might have seen a demo video like this:
