Like many developers exploring the world of wearables, I’m an avid fan of apps that track your exercise. Companies like Strava & Runkeeper have paved the way for what we can do with motion and health data, creating new and unique experiences that attract millions of users. However, I was always frustrated that I could automatically track my runs, but I still had to enter each gym set by hand.
Continue reading Introduction to Apple WatchKit with Core Motion — Tracking Jumping Jacks
In this tutorial, we’ll cover how to convert a model in PyTorch to the ONNX format and then load it to Caffe2. We’ll then use Caffe2’s mobile exporter to execute it on mobile.
Continue reading Using ONNX to Transfer Machine Learning Models from PyTorch to Caffe2 and Mobile
Apple’s SwiftUI announcement during last year’s WWDC (2019) was a welcomed surprise for Apple devs. The framework embraces a more declarative and reactive approach to building user interfaces—a complete paradigm shift from interface builder and storyboards. In a way, SwiftUI brings the joys of Swift to UI-building, signaling a coming departure of Objective-C inspired systems.
SwiftUI 1.0 (unofficial versioning), though, proved to still be somewhat a prototype framework, showing signs of its infancy. There were:
Frontier Design’s Video Star is a freemium video editing and creation app with a rich, deep toolkit that allows users to become the stars of their own music videos. From transitions, to blur and color effects, to adding 3D objects and text, and beyond, Video Star has a seemingly endless trove of fun capabilities to explore.
Vertex animation is the keyframed animation of individual vertices of a mesh. It is very beneficial for very complicated motion like that of cloth, hair, or water. It uses a flex controller bone to move the model through a series of flexes — one for each frame.
Vertex animation is included in almost all video or computer-based games. To animate the character, one just needs to vary the sampled line based on the current time.
Continue reading Vertex Animation in Lens Studio for Snapchat Lenses
Ever wondered what the 27th letter in the English alphabet might look like? Or how your appearance would be twenty years from now? Or perhaps how that super-grumpy professor of yours might look with a big, wide smile on his face?
Thanks to machine learning, all this is not only possible, but relatively easy to do with the inference of a powerful neural network (rather than hours spent on Photoshop). The neural networks that make this possible are termed adversarial networks. Often described as one of the coolest concepts in machine learning, they are actually a set of more than one network (usually two) which are continually competing with each other (hence, adversarially), producing some interesting results along the way.
Continue reading StyleGAN: Use machine learning to generate and customize realistic images
ML Kit by google is yet another very efficient and clean way of integrating basic machine learning into your mobile apps. It offers some easy to use prebuilt base APIs:
The best part is that you can use these services on-device (without needing any internet connection for external server calls) or on-cloud (wider training base requiring server calls). Most importantly, this can be integrated in both iOS and Android apps.
Continue reading Text Recognition and Translation on iOS Using ML Kit and Google Translate
With React Hooks growing in usage, the ability to handle a component’s state and side effects is now a common pattern in functional components. React Redux offers a set of Hook APIs as an alternative to the omnipresent connect() high order component.
In this tutorial, let’s continue building a simple React Native app where a user can save notes. In part 1, we used the Redux Hooks API to manage state. This post is in continuation of that previous post:
Continue reading Using Redux with React Hooks in a React Native app
If you follow me, you know that this year I started a series called Weekly Digest for Data Science and AI: Python & R, where I highlighted the best libraries, repos, packages, and tools that help us be better data scientists for all kinds of tasks.
The great folks at Heartbeat sponsored a lot of these digests, and they asked me to create a list of the best of the best—those libraries that really changed or improved the way we worked this year (and beyond).
Continue reading Top 7 Libraries and Packages for Data Science and AI: Python & R
Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints. It’s also important to note that pose estimation has various sub-tasks such as single pose estimation, estimating poses in an image with many people, estimating poses in crowded places, and estimating poses in videos.
