Entity extraction can be useful when you want to add interactivity to your application based on the context of the text.
For example, if it’s a phone number you can prompt the user to make a call and if it’s an email address you can prompt the user to open the email app. This is achieved by first extracting various entities in the text. In this piece, let’s look at how that can be achieved using Google’s ML Kit.
First things first—
What is Visual Recognition? And where can it be used??In short, Visual Recognition helps us find meaning in visual content.
We can use Visual Recognition to develop smart applications that analyze the visual content of images or video frames to understand what is happening in a scene.
Continue reading “Visual Recognition in Android Using IBM Watson”
Machine learning and AI are taking mobile application development to a new level. Apps that utilizing machine learning can recognize speech, images, and gestures.
This gives us new and compelling ways to engage and interact with people in the world around us. But how do we integrate machine learning into our mobile apps?
Developing mobile applications that incorporate machine learning has long been a difficult task. But with the help of platforms and dev tools such as Fritz AI, Firebase’s ML, and TensorFlow Lite, it’s getting easier to do so.
Continue reading “Digit Recognizer with Flutter and TensorFlow Lite”
Image and video compression techniques have greatly advanced in recent years. However, most compression techniques still can’t handle the massive growth in media data.
Read on to learn how deep learning (DL) can help solve the challenges of traditional compression frameworks.
Continue reading “How Deep Learning Solves Compression Challenges”
With the advent of neural networks, machine learning has gained immense popularity, and companies in just about every industry have started to apply some form of this vast technology to increase efficiency, improve throughput, or enhance customer experiences.
Artificial intelligence as a field has seen major breakthroughs in many areas within the past decade. With so many industries jumping towards automation and trying to apply AI to enhance customer experiences, it’s started to create a bigger impact in our day-to-day lives.
Being used on such a large and varied scale, it has recently come to light that these methods come with their own problems.
As global food demand continues to rise and myriad threats of climate change intensify, creating more sustainable agricultural practices has become increasingly essential. This is especially true in remote areas of the world, where advanced agricultural expertise is scarce and smallholder farmers with limited resources (both financial and material) cultivate an estimated 80% of farmland.
Continue reading “PlantVillage: Helping farmers in East Africa detect and treat plant disease”
In this article, I’ll show you how to build your own real-time object detection iOS app. Thanks to other people’s articles, you can easily train your own object recognition model using TensorFlow’s Object Detection API and integrate the trained model into your iOS app.
Continue reading “Building a Real-time Object Detection iOS App That Detects Sushi”
As a mobile developer looking to integrate computer vision-based machine learning in your app(s), adding camera functionality is one of the most crucial aspects of the entire process.
You not only need the library to be stable and lightweight, but that library should also support the vast array of Android devices out there, most of which have slightly different camera implementations.
Continue reading “CameraX: _The_ Machine Learning Camera Library for Android”
This blog is the fourth one in my series on training and running Tensorflow models in a Python environment. If you haven’t read my earlier blogs centered on AutoML and machine learning on edge devices, I’d suggest that you do so before continuing with this post.
Continue reading “Using Google Cloud AutoML Edge Object Detection Models in Python”
This is the ninth post in my series around training and running Cloud AutoML models on the edge. This post follows up from the post earlier on training a multi-label image classification model and covers how to run the trained model in a python environment.
Continue reading “Using Google Cloud AutoML Multi-Label Image Classification Models in Python”
