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You may recall that Apple acquired the machine learning and artificial intelligence startup Turi a few years ago for upwards of $200M; it offers powerful tools to create advanced machine learning models in a short amount of time.

In this tutorial, you’ll be learning to install Turi Create on your Mac, create a Python script, and use that script to train a Core ML model that you can drag directly into your Xcode projects and quickly implement in your apps.

Continue reading Training a Core ML Model with Turi Create to Classify Dog Breeds

My initial interest in mobile machine learning came from a somewhat strange place. In short, a friend of mine approached me with an idea for an application that would apply a NSFW filter to online videos. This led to a two-week deep dive into the tools to make this happen:

The framework to be used to train the model (TensorFlow, Keras, Turi Create, etc.) The platform to launch this framework for training purposes (Google Colab, FloydHub, Conda, etc.) The programs to be used to markup photos (RectLabel, LabelImg) Python scripts to be used to convert these annotations in the format required by the framework

In the end, I collected a dataset of 100 photos which I found on the Internet (don’t ask), and I succeeded in training my first object detection model ready for mobile.

Continue reading “Train and Ship a Core ML Object Detection Model for iOS in 4 Hours — Without a Line of Code”

Speech recognition is invading our lives. It’s built into our phones (Siri), our game consoles (Kinect), our smartwatches (Apple Watch), and even our homes (Amazon Echo). But speech recognition has been around for decades, so why is it just now hitting the mainstream?

The reason is that deep learning finally made speech recognition accurate enough to be useful outside of carefully-controlled environments. In this blog post, we’ll learn how to perform speech recognition with 3 different implementations of popular deep learning frameworks.

Continue reading The 3 Deep Learning Frameworks For End-to-End Speech Recognition That Power Your Devices

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.

Continue reading SharedPreferences in Android

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.

Continue reading Solving equations using neural networks: Exploring Facebook AI’s latest research effort

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?

Continue reading Spam Filtering Using Bag-of-Words

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