Computer vision is a branch of deep learning that focuses on the utilization of deep neural networks to model problems from images. In this article, we’ll be looking at how we can apply computer vision as a tool for football analytics.
Football is a sport that involves 2 teams; with each team having 11 players and a goalkeeper. Here are some analytics that could be explored from football games using AI.
Continue reading Multi-team object detection for football games on Raspberry Pi 3
Creating accurate machine learning models capable of identifying multiple faces (or other target objects) in a single image remains a core challenge in computer vision, but now with the advancement of deep learning and computer vision models for mobile, you can much more easily detect faces in an image.
In this article, we’ll do just that on Android with the help of Google ML Kit’s Face Detection API.
Continue reading On-Device Face Detection on Android using Google’s ML Kit
Today we’re announcing a partnership with ModelDepot to provide pre-trained machine learning models converted specifically for use in mobile apps. Three newly-converted models are now available in Core ML format for use in iOS applications. This is just the start, and over time we’ll add additional models and additional ML platforms.
Continue reading ModelDepot and Fritz AI Partner to Provide Mobile-friendly ML Models
Skin care is a really personal thing. But there are significant roadblocks in the way of accessing treatment. From a shortage of dermatologists (a recent study found there were just over 3 per 100,000 people), to cost-prohibitive treatment options, people struggling with skin conditions often have nowhere to turn.
Continue reading MDacne uses mobile machine learning to offer customized skin care plans
Did you know that 60% of newly-launched products may not perform well because they fail to represent or actually offer something, their customers really want?
This is the era of personalization. Using personalization you can efficiently attract new customers and retain existing customers. These days, a one-size-fits-all approach generally doesn’t work.
Personalization starts with customer segmentation, which is the practice of grouping customers based on features like age, gender, interests, and spending habits. We do this so we can customize our marketing approaches for each customer group.
Continue reading K-means clustering using sklearn and Python
OpenCV is a powerful tool for doing intensive operations in real-time, even for resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various effects to images.
In part 1 of the series, we discussed horizontal and vertical image stitching:
This tutorial will explore creating a cartoon image effect by reducing the number of colors representing the image using lookup tables (LUTs). The sections covered in this tutorial are as follows:
Continue reading Image Effects for Android using OpenCV: Cartoon Effect
Phone authentication allows a user to sign in to a mobile app using their phone number. This could be done traditionally by associating a password with a user and storing it when the user first registers with the app.
Continue reading Implement Firebase Phone Authentication in React Native Apps
Nowadays, building a machine learning app isn’t considered nearly as difficult as it used to be. We have numerous cloud providers offering services that enable us to use their ML service to train and deploy models to a variety of environments and devices.
In this tutorial, we’re going to use TensorFlow.js to classify an apple as rotten or fresh using a fruit image dataset from Kaggle. We’re going to train our model using Microsoft Custom Vision from the Azure software family. Lastly, we’ll implement a simple Node.js application in order to handle the classification task directly on the browser.
Continue reading Image Classification Made Easy in the Browser with TensorFlow.js
Machine learning is rapidly moving from manually designed models to automated data pipelines using tools such as auto-sklearn, MLbox, and TPOT. In using these automated tools, the aim is to simplify the model selection process and come up with the best data set features for our model.
Continue reading Introduction to Automated Feature Engineering Using Deep Feature Synthesis (DFS)
In this article, we’ll explore a range of debugging techniques that can be used while developing in Android Studio. As developers, we all know that there are days where you spend way more time in your debugger than you do in your code editor.
If you don’t know what debugging actually is then check out the link below:
Continue reading Tips & Tricks for Debugging in Android Studio
