Category: Articles

Transfer learning, an approach where a model developed for a task is reused as the starting point for a model on a second task, is an important approach in machine learning. Prior knowledge from one domain and task is leveraged into a different domain and task.

Transfer learning, therefore, draws inspiration from human beings, who are capable of transferring and leveraging knowledge from what they have learned in the past for tackling a wide variety of tasks.

Continue reading “Using Transfer Learning and Pre-trained Language Models to Classify Spam”

Lens Studio allows you to import and add audio to your lenses. Adding audio to your lenses enhances the nature of the lenses by enabling the viewer more interaction. It can be added in behavior scripts as a response and even in animations.

Behavior scripts in lens studio enable you to make interactions in your lenses by defining a trigger type and a response for that trigger.

Continue reading “Audio in Lens Studio”

OpenCV is a powerful tool for doing intensive operations in real-time, even for resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various effects over images.

Continue reading “Image Effects for Android using OpenCV: Animated GIFs”

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”