Following up on my last blog post on training an image labeling model using Google Cloud AutoML (linked below), in this second blog post in the series; we’ll look into how to train yet another model to identify and locate objects within an image instead—an object detection model!
If you haven’t read my blog on image labeling, you can read it here:
Continue reading Creating a TensorFlow Lite Object Detection Model using Google Cloud AutoML
In 2015, Snapchat, the incredibly popular social content platform, added Lenses to their mobile app —augmented reality (AR) filters that give you big strange teeth, turn your face into an alpaca, or trigger digital brand-based experiences.
In addition to AR, the other core underlying technology in Lenses is mobile machine learning — neural networks running on-device that do things like create a precise map of your face or separate an image/video’s background from its foreground.
Continue reading Creating a Style Transfer Snapchat Lens with Fritz AI and SnapML in Lens Studio
AR (augmented reality) is the next-gen thing. It provides an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities including visual, auditory, and haptic.
Facebook’s Spark AR Studio is a tool for creators to create, publish, and manage AR Apps. It is an open-source tool by Facebook and anyone with a Facebook account can easily access this studio. Nowadays, it is mostly used for creating AR filters for Facebook and Instagram.
Continue reading Create and Publish Your First Instagram AR Filter Using Spark AR Studio
Python remains the most popular language for building and training machine/deep learning models. This is because of the numerous libraries and tools built around it, that enables developers and researchers to quickly build models.
But in terms of deployment of these models created in Python, there is a trend towards using a different language. Some of the reasons behind this are:
Continue reading Converting TensorFlow / Keras models built in Python to JavaScript
Formik and Yup are great development tools for building awesome looking UI forms in React Native apps. To showcase the power and benefits of these tools, we’ll build two forms for login and signup screens, and show how easy it is to validate them using both Formik and Yup.
Continue reading Build and validate forms in React Native using Formik and Yup
Math might be scary, but it’s an essential part of everyday life. Wouldn’t it be cool if we could build an app, point our phone’s camera at an expression, and let the app compute the result? Whenever I’ve needed to use math, I’ve wished this was possible. Now, with advances in machine learning and vision recognition in iOS, this is doable.
Continue reading Building an iOS camera calculator with Core ML’s Vision and Tesseract OCR
In part 3 of our tutorial series on building a chat app in React Native, we integrated Firestore the current React Native app, creating a database that stores chat room names. A new chat room can be created using a modal stack, but only if a given user is authenticated.
Machine learning has expanded computers’ ability to understand images and extract different information from visual data. In this article, different computer vision tasks will be presented alongside explanations for how each has been tackled using machine learning.
A lot of machine learning research has been done in the field of computer vision throughout the last 3 decades. Different topics, tasks, and problems have been studied thoroughly; however, we’ll focus on the core problems of computer vision, and we’ll briefly present some of the more advanced hot topics in computer vision towards the end.
Continue reading 6 Significant Computer Vision Problems Solved by ML
If you follow my writing (here or here), then you know I’ve been working on a new Android app called AfterShoot — it’s an AI-powered Android app that helps users take better pictures while also managing their digital waste.
One of the important features that I had to implement in this app was the ability to handle feedback provided by users — meaning, if my trained model predicted an incorrect result, I should be able to take that feedback from the user about what the correct output should be and use that to retrain my model for better results.
Continue reading “Working with Firebase Storage in Android: Part 1”
As the AI boom progresses, there is a definite transition, moving intelligent processes to end devices from their original home in the cloud and big data centers with tremendous compute power.
Organizations all across the globe are recognizing the need for machine learning practices on mobile devices like smartphones and the ability to perform powerful AI-enabled tasks natively on gadgets, machines, vehicles, and more. Qualcomm is one such tech giant trying to shape the edge AI sector with its new line of ML offerings.
