Core ML is the most popular and powerful framework for adding machine learning and AI to iOS apps. High-level APIs provided by tools like Turi Create and Create ML make it possible to train mobile-friendly models without ML expertise.
But as projects grow in scale and complexity, it’s often necessary to dive deeper into the capabilities of Core ML to deliver the best user experiences possible.
In this article, we’ll build a simple neural network using Keras. We’ll assume you have prior knowledge of machine learning packages such as scikit-learn and other scientific packages such as Pandas and Numpy.
The journey to AR filters wasn’t easy for Paris-born Rocha, but the creator, who studied Art History at the Sorbonne University, is making the most of it. Now making filters for clients and for pleasure, the former makeup artist is making waves in filter innovation. I learned more about the artist and the story behind her incredible, fun, and surreal filters.
In this tutorial you will learn how to work with hair segmentation effects by creating texture and adding interactivity to your filters using Spark AR Studio.
Continue reading “How to Create Hair Segmentation Effects Using Spark AR Studio”
Recently, Snapchat released a new feature where AI developers can “easily” integrate their own custom models—one of which is the ability to classify single objects in order to trigger fun and immersive AR effects. We’ll cover this ML task in this article.
Lens Studio, an augmented reality creativity suite released by Snapchat in 2017, gives users the opportunity to create their own augmented reality (AR) Lenses.
Continue reading “Create a Custom AI-Powered Snapchat Lens with Fritz AI Studio”
This year, Apple introduced Core ML 2 at WWDC 2018, with a focus on making machine learning more flexible and powerful for developers to use.
With the release of this new and improved framework, Apple also announced a freshly updated version of coremltools. This handy Python library can be used to convert trained models into a Core ML format as well as making predictions directly from your machine.
Continue reading “Reducing Core ML 2 Model Size by 4X Using Quantization in iOS 12”
In this piece, we’ll look at some of the AI and machine learning conferences that took place in 2018 and highlight some of the best speakers and presentations.
These events form a fundamental part of the machine learning and AI communities because they bring people together to learn from each other as well as forge meaningful collaborations.
We’ll also mention the possible dates for some these conferences so you can look out for them in the coming year.
Continue reading “The Top AI & Machine Learning Conferences”
This blog post is a continuation of a series centered on the development work I’ve been doing on the AfterShoot app.
If you haven’t read the earlier ones, you can find a few of them here:
Whenever you’re dealing with images in an app, it’s likely that you’ll want to give your users options to perform certain actions on multiple images at once. For example, in a Gallery app, users are commonly able to select multiple images and delete them all at once instead of performing the delete operation on every image one-by-one.
Continue reading “Implementing a multi-select RecyclerView with a dynamic ActionBar in Android”
AI is changing the way we shop, and just in time. Among these changes, we’re seeing that the need for intuitive and intelligent mobile commerce solutions has never been more stark. In the U.S., retail sales were down 8.1% year-over-year as of June 2020—to be expected as consumers stay home and save during tenuous economic times (to say the least).
Continue reading “Visual Search for Mobile Commerce in Action: Shnap”
In 2019 Apple introduced some really exciting features and improvements to its Vision framework. Through these changes, Apple showed us that alongside on-device machine learning, computer vision is an equally important part of their arsenal for mobile developers looking to build smart and intelligent applications.
For readers new to the Vision framework, it aims to provide a high-level API for complex computer vision algorithms, as well as act as a catalyst for Core ML models.
