Thus far in our series on SwiftUI, our demo project consists of a dynamic profile page that’s ready to take in any Person and display their attributes. In this tutorial, we’ll get to actually make a list of folks that we can scroll through using a List and, when we select one, navigate to their detailed profile page!
Continue reading SwiftUI: Lists, Navigation, and Detail Views
This tutorial is a step-by-step guide for building an artificial neural network (ANN) using OpenCV for Android devices. An ANN for creating a 2-input XOR gate is built and trained on a desktop computer and then saved for later use in an Android app.
The IDE used in this tutorial for building the desktop app is NetBeans and the OS is Windows. This tutorial discusses the steps for using OpenCV in NetBeans.
Continue reading Running Artificial Neural Networks in Android using OpenCV
Android provides many ways of storing an application’s data. One of these ways is called SharedPreferences. SharedPreferences allow you to save and retrieve data in the form of a key-value pair. It’s beneficial to use SharedPreferences APIs only when you have a relatively small collection of key-values that you’d like to save. Each SharedPreferences file is managed by the framework and can be private or shared.
Computer systems have always been man’s best friend when dealing with intensive, computation-heavy mathematical problems. From simple calculations on a calculator to large statistical operations in R, this technological frontier has made life easier for a lot of us.
However, even computers are known to fault when numbers turn into letters and algebra starts getting involved. One might hope that with the advent of increasingly sophisticated machine learning and AI algorithms, this could be solved—but hopes and dreams continued to remain hopes and dreams…until now.
In this post, we’re going to employ one simple natural language processing (NLP) algorithm known as bag-of-words to classify messages as ham or spam. Using bag of words and feature engineering related to NLP, we’ll get hands-on experience on a small dataset for SMS classification.
So, what are we waiting for?
Apple has recently been pushing the envelope with regards to Artificial Intelligence, and WWDC 2020 was no different.
From machine and deep learning to computer vision and natural language processing, Apple’s introduced a slew of enhancements and improvements across their built-in frameworks that help mobile application developers build better AI-powered iOS apps.
PencilKit, a drawing framework that was introduced in iOS 13, was also powered with machine learning this year.
Continue reading Advancements in Artificial Intelligence in iOS 14
I’ve never quite attended an event like Lens Fest. Not just because it was entirely virtual, but largely because of the community of people it was celebrating — AR developers, graphic designers, 3D artists, software engineers, ML engineers, and a wide range of other people from around the world who, put simply, create really cool stuff. I was blown away by all the amazing experiences people from all walks of life are building with Lens Studio.
In this piece, we’ll look at some of the top open source machine learning projects in 2019, as ranked by MyBridge.
This project is an implementation of the SV2TTS paper with a vocoder that works in real-time. Using this repo, one is able to clone a voice in 5 seconds to generate arbitrary speech in real-time.
Continue reading 2019’s Top Open Source Machine Learning Projects
Having had the privilege of compiling a wide range of articles exploring state-of-art machine and deep learning research in 2019 (you can find many of them here), I wanted to take a moment to highlight the ones that I found most interesting. I’ll also share links to their code implementations so that you can try your hands at them.
Continue reading Top Machine and Deep Learning Research Papers
Most of the attention, when it comes to machine learning or deep learning models, is given to computer vision or natural language sub-domain problems.
However, there’s an ever-increasing need to process audio data, with emerging advancements in technologies like Google Home and Alexa that extract information from voice signals. As such, working with audio data has become a new trend and area of study.
Continue reading Working with Audio Data for Machine Learning in Python
