Category: Articles

Augmented reality (AR) and artificial intelligence (AI) are two of the most promising technologies available to mobile app developers. Huge hype cycles and rapidly evolving tools, though, have blurred the lines between the two, making it difficult to tell where AI ends and AR begins. This post aims to disambiguate AR and AI. It covers how AR and AI work together, the current state of SDKs and APIs for each, and some practical ways to combine them to build incredible mobile experiences.

Continue reading Combining artificial intelligence and augmented reality in mobile apps

In this project, we’ll use the react-native-maps and react-native-background-geolocation package, a group of React Native map components for iOS + Android that we’ll use to configure a React Native location tracking application.

Here, we’ll focus on building the tracking app project for iOS. The library ships with platform native code that needs to be compiled together with React Native. This requires you to configure your build tools.

Continue reading Building a React Native Background Location Tracking App

Artificial intelligence (AI) is a buzzword you see pretty much everywhere around you, even when you’re not looking. It has completely dominated tech media, newsrooms, and is even credited with the success of many modern applications.

But does it really work, or is it just hype? Truth is, it does. While there might be some hype around its capabilities, AI has been demonstrated both in research and industry to work really well for a variety of tasks and use cases.

Continue reading Building a Neural Network From Scratch Using Python (Part 1)

In this tutorial, I’ll walk through how to start building an Augmented Reality Android application using Google’s new Sceneform SDK. First off, I’ll cover some quick concepts and then jump into project setup and some code. If you just want to dive in straight away, you can check out my GitHub repo below!

Continue reading Build your first Android AR app with ARCore and Sceneform in 5 minutes*

With the immense growth of videos on the internet, it’s become really difficult to efficiently search amongst millions of them. When searching for an event query, users are often bewildered by the vast quantity of videos returned by search engines like Google. Exploring such results can be time-consuming and can also degrade the user experience.

Hence, we’ll be discussing ways to automate this process with machine learning-based video summarization techniques.

Continue reading Deep learning-based video summarization — A detailed exploration

Whether you’re looking to touch up a family portrait or create a surreal masterpiece, Superimpose X’s expansive photo editing toolkit will help you realize your vision. Superimpose X inspires creative approaches with 3 primary sets of editing tools: layers, masks, and effects like blurs, color corrections, smudges, and more.

Continue reading Community Spotlight: Superimpose X

We live in the world that’s still transitioning from inventions of the Renaissance to inventions of the modern technological era. A couple examples: We still exchange business cards during meetings but store our numbers on cell phone; we still receive paper bills for our electricity, gas, and other utilities but pay them through mobile accounts. The list goes on and on. With the growing advances in machine learning, many steps of these processes can be eradicated right away.

Continue reading Building Text Detection apps for iOS and Android using React Native

If you’re a fan of HBO’s Silicon Valley, you’ll remember when they launched a real AI-powered mobile app that classifies hotdogs from a given image (or not). Using Google’s Vision API, let’s try to recreate a working model of the application in React Native.

Google’s Vision API is a machine learning tool that classifies details from an image provided as an input. The process of these classifications is based on thousands of different categories that are included in pre-trained API models. The Vision API enables access to these pre-trained models via a REST API.

Continue reading Build a “Not Hotdog” clone with React Native

In this article, we will continue building production machine learning systems on GCP with a special focus on the design of hybrid ML systems which covers the following:

Building hybrid cloud machine learning models Kubeflow for hybrid cloud Optimize Tensorflow graphs for mobile

Continue reading “Building Production Machine Learning Systems on Google Cloud Platform”

Complex models such as deep neural networks are prone to overfitting because of their flexibility in memorizing the idiosyncratic patterns in the training set, instead of generalizing to unseen data.

Any modification we make to a learning algorithm that’s intended to reduce its generalization error but not its training error is called regularization. Keeping the model simple enough by using regularization techniques allows the network to generalize well on data points it hasn’t seen before.

Continue reading Deep Learning Best Practices: Regularization Techniques for Better Neural Network Performance