Machine learning is often viewed through the lens of transformation, whether it’s reshaping an industry, automating tedious tasks, or inspiring immersive experiences.
The latter transformation is front and center in Hart Woolery’s InstaSaber. It’s an ML-powered iOS app that lets users take a rolled up piece of paper and, by holding it in front of the phone’s camera, generate a realistic augmented reality (AR) lightsaber that’s highly-responsive to real-time hand movement.
With 3D Motion Sense technology already out on Pixel 4, it looks like our way of interacting with phones is going to change soon. Touchless interactions have a promising future for sure, with Apple’s most ambitious product to date— AR glasses—already under development.
Model evaluation is the process of choosing between models, different model types, tuning parameters, and features. Better evaluation processes lead to better, more accurate models in your applications.
In this article, we’ll be discussing model evaluation for supervised classification models. We’ll cover evaluation procedures, evaluation metrics, and where to apply them.
You built this amazing machine learning model—this one, let’s say—but now what?
How do you take your model and turn it into something that you can display on the web? How do you turn it into something that other people can interact with? How do you make it useful?
You deploy it!
Continue reading “The brilliant beginner’s guide to model deployment”
People are passionate about pizza. Deep dish, thin crust, traditional pan, flatbread — and those are just styles of crust, to say nothing of topping preferences. Pineapple? Anchovies??
With such a wide variety of styles and preferences to swear by, it’s important for pizza lovers to have an authoritative source for honest and legitimate reviews — and the chance to share their love of pizza with others.
Continue reading “One Bite pizza reviews: Building community trust with mobile machine learning”
WebViews in Android are often seen as a lazy programmer’s tool—Don’t have enough time to build a particular screen for your app? Just use a WebView to display a page from your website instead. Users won’t be able to tell the difference…right?
Jokes aside, while you’ll often hear other developers discouraging the use of WebViews, they’re often necessary. For example, if you want to make a transaction with your bank or credit card, that transaction screen is a WebView.
Custom login screen for apps where security is a concern is often a WebView instead of a native widget. Google Mobile Ads (or Admob) also uses a WebView to display Banner and Interstitial ads on your app.
If you’re looking for help in easily adding machine learning to your Android apps, you’ve come to the right place. In this article, we’ll learn how to get started, implementing a task known as text recognition.
Continue reading “Recognize Text in Images on Android with Firebase’s ML Kit”
I’ve always thought of community as something akin to a party. A thing that forms largely by happenstance, where the people involved are connected through discrete and clear purposes: Socializing, relaxing after a long work week, meeting new people.
So when I found myself in the role of building the Heartbeat community, I initially struggled to see a bigger picture. I had the vague sense that the main priority should be centered on putting the needs and desires of community members first—much in the same way I’d want to be a good host at a party.
There exist a plethora of articles on quantization, but they generally cover only the surface level theory or provide a simple overview. In this article, I’ll explain how quantization is actually implemented by deep learning frameworks.
Before getting into quantization, it’s good to understand the basic difference between two key concepts: float (floating-point) and int (fixed-point).
I’m by no means a developer, engineer, or coder of any kind. Though I do read a lot of tutorials that include code implementations, and occasionally peruse repos to explore what folks are building, the most I’ve ever done is add some CSS style elements to a button within an HTML file.
So on the surface, the idea of me being able to build a cross-platform, mobile-ready machine learning model is kind of absurd.
