Category: Articles

Deep Learning algorithms are excellent at solving very complex problems, including Image Recognition, Object Detection, Language Translation, Speech Recognition, and Synthesis, and include many more applications, such as Generative Models.

However, deep learning is extremely compute intensive—it’s generally only viable through acceleration by powerful general-purpose GPUs, especially from Nvidia. Unfortunately, mobile devices have very limited compute capacity; hence, most architectures that have been very successful on desktop computers and servers cannot be directly deployed to mobile devices.

Continue reading “Building an Image Recognition Model for Mobile using Depthwise Convolutions”

With big industry players committing a lot of resources to edge ML, increases in research into model compression/optimization for resource-constrained devices, and an ever-expanding landscape of use cases, we’ve seen the the initial stages of what we believe is a revolution in how and where machine learning operates.

Continue reading “Machine Learning on Mobile and at the Edge”

If you’re even remotely plugged into the tech world, you’d have been hard-pressed to miss a couple viral summer trends, both involving AI-powered photo transformations.

Here’s the gist. Snapchat caught fire and soared past all Q2 estimates, in large part because of their rollout out of popular gender-swap and baby-face Lenses. And soon after, FaceApp took the internet by storm when its old-age filter went viral. This has led to millions of users, including prominent celebrities, showing off what they’d look like in 40 years or as members of a different gender.

Continue reading “Snapchat, FaceApp, and the necessary lessons of data privacy with mobile machine learning”

TensorFlow Lite is the lightweight version of TensorFlow Mobile. It’s here to unleash the machine learning power on your smartphones while ensuring that the model binary size isn’t too big and there’s low latency. Additionally, it also supports hardware acceleration using the Neural Networks API and is destined to run 4X faster with GPU support.

Continue reading “Image Classification on Android with TensorFlow Lite and CameraX”

Convolutional neural networks are a class of deep neural networks that are commonly used for visual imagery analysis. One of their main applications is in image classification.

Continue reading “A Research Guide to Convolutional Neural Networks”

Learning and implementing different AI-powered apps using the TensorFlow.js library empowers you to do so many amazing things with ML in the browser.

This tutorial is the latest in my series using TensorFlow.js for machine learning and implementing those models in React apps. Here, we’ll learn about another TensorFlow library that helps with body segmentation.

Continue reading “Body Segmentation in the Browser with TensorFlow.js”

Visual scripting is one of the ways that you can add interactivity to your lenses. It is node-based, enabling you to see the logic as you are creating. You also don’t need to write code.

In this article, you will see how responses can be executed when certain events are enabled using node-based visual scripting.

Continue reading “Visual Scripting in Lens Studio”

Style transfer is a very popular deep learning task that lets you change an image’s composition by applying the visual style of another image.

From building artistic photo editors to giving a new look to your game designs through state-of-the-art themes, there are plenty of amazing things you can build with neural style transfer models. It’s also can be handy or data augmentation.

Continue reading “Running Create ML Style Transfer Models in an iOS Camera Application”

When he first conceived of Momento, founder Genday Okrain set out to build an experience that would allow users to look back on memories since-passed. The result was a mobile app that could transform photos, live photos, and videos into shareable GIFs.

Continue reading “Momento: Animating memories with immersive GIFs powered by mobile machine learning”