Category: Articles

Core ML is an interesting means for adding a pre-trained machine learning model to your app. But one question that nagged me after trying Core ML was—How can I possibly train my own model and integrate it in my apps using Core ML?

Well, after doing some homework, a learned a lot about the possibilities of achieving this. To be honest, all the methods require you to understand and know your math really well! While I was on this roller coaster ride, I came across Custom Vision.

What a relief for developers looking to jump straight into training their own machine learning models. With the help of Custom Vision, developers can easily manifest their machine learning ideas into real mobile apps without diving too deeply into the machine learning waters.

Continue reading “Using Core ML and Custom Vision to Build a Real-Time Hand Sign Detector in iOS”

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”

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”

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.

Continue reading “Breaking Privacy in Federated Learning”