This tutorial is the 9th part in the series, ML Kit for Mobile Developers. If you’re not quite caught up, you can start here:
Earlier this month at Google I/O, the team behind Firebase ML Kit announced the addition of 2 new APIs into their arsenal: object detection and an on-device translation API.
Continue reading Building a real-time object detection app on Android using Firebase ML Kit
In this tutorial, we’ll learn the basics of creating an intro slider using React Native. To do this, we’ll use a React Native module known as react-native-app-walkthrough, and the result will be a simple and configurable app introduction slider.
The detailed explanation of each step for this tutorial is provided below:
First, we need to simply install the react-native-app-walkthrough package. We can do this using NPM (Node Package Manager) or yarn, and for this, we’ll using NPM. Open your NPM console or command prompt within your project directory and enter the following code snippet:
Continue reading Building an App Introduction Slider in React Native
All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”. The group of functions that are minimized are called “loss functions”. A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome. A most commonly used method of finding the minimum point of function is “gradient descent”. Think of loss function like undulating mountain and gradient descent is like sliding down the mountain to reach the bottommost point.
Continue reading 5 Regression Loss Functions All Machine Learners Should Know
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few.
We can think of semantic segmentation as image classification at a pixel level. For example, in an image that has many cars, segmentation will label all the objects as car objects. However, a separate class of models known as instance segmentation is able to label the separate instances where an object appears in an image. This kind of segmentation can be very useful in applications that are used to count the number of objects, such as counting the amount of foot traffic in a mall.
This guide describes how to process audio files in Android, in order to feed them into deep learning models built using TensorFlow.
TensorFlow Lite’s launch and subsequent progress have reduced the distance between mobile development and AI. And over time, don’t be surprised if app stores eventually end up flooded with AI/ML-powered apps.
Continue reading A Definitive Guide for Audio Processing in Android with TensorFlow Lite Models
This tutorial guides Android developers in preparing the popular library OpenCV (Open Computer Vision) for use. Through a step-by-step guide, the library will be imported into Android Studio (the official IDE for Android).
Upon installation and setup, OpenCV can be used for performing any of the operations it supports, such as object detection, segmentation, tracking, and more.
Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision.
Continue reading 3D Face Reconstruction with Position Map Regression Networks
According to Snapchat’s founders, the smartphone camera has always been at the heart of their app. Over the years, Snapchat has evolved to do much more to your photos than simple image and video messaging.
This giant social media platform is most popular among young people, and the main reason for this is its amazing photo/video editing tools and a wide variety of available camera filters.
Continue reading Creating Snapchat Augmented Reality Lenses in Lens Studio—A Beginner’s Guide
I was inspired by this example of Core ML + ARKit. But I found one significant disadvantage — it doesn’t place annotations on objects automatically. Instead, you need to center the object in your camera view and use your finger to place an annotation.
In my opinion, this destroys user expectations, so I decided to fix that and build a more immersive user experience using object detection in augmented reality.
Continue reading Core ML + ARKit: Annotating Objects in Augmented Reality
One of the most popular languages in the data science and machine learning world is Python, and most Python tasks usually involve at least 5 packages that need to be installed. However, these packages typically have their own dependency packages for them to function well. And some of these dependency packages must be in a specific version. This is where a Python virtual environment comes into play.
Continue reading Creating Python Virtual Environments with Conda: Why and How
