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.
This article is about building a neural machine translation web app and then actually deploying it.
Let’s Get started:
After spending a lot of time gathering and cleaning data and developing and tuning your model, it’s now time to deploy the model and make it ready for your users to interact with.
Continue reading “Deploying your Language Model with Google Cloud”
The advent of machine learning on mobile has opened doors for a bunch of new opportunities. While it has allowed ML experts to tap into the mobile space, the other end of that equation is actually the show-stealer. Letting mobile application developers dabble in machine learning has actually made its with mobile application development so exciting.
The best thing is, that you needn’t be a machine learning expert in order to train or run models. Core ML, Apple’s machine learning framework, provides an easy-to-use API that lets you run inference (model predictions), fine-tune models, or re-train on the device.
Continue reading “Build a SwiftUI + Core ML Emoji Hunt Game for iOS”
The sports world is an entertainment business at its core, and technology is starting to play a more important role in the expansion of this business. From the NHL to the NFL, MLB, NBA and NASCAR, virtually every major U.S. sports league is now incorporating AI to expand its bottom line.
The North American sports industry is expected to rake in $73.5 billion in revenue by 2019 in the form of gate revenues, merchandising, sponsorships, and media rights.
Continue reading “4 Ways AI Is Changing How We Watch and Play Sports”
Because it’s really GOOD.
Indeed, XGBoost, a gradient boosting algorithm, has been consistently employed in the winning solutions in Kaggle competitions involving structured data. XGBoost has excellent precision and adapts well to all types of data and problems, making it the ideal algorithm when performance and speed take precedence.
Continue reading “Introduction to XGBoost with an Implementation in an iOS Application”
From training to experimenting with different parameters, the process of designing neural networks is labor-intensive, challenging, and often cumbersome. But imagine if it was possible to automate this process. That imaginative leap-turned-reality forms the basis of this guide.
We’ll explore a range of research papers that have sought to solve the challenging task of automating neural network design.
In this guide, we assume that the reader has been involved in the process of designing neural networks from scratch using one of the frameworks such as Keras or TensorFlow.
Continue reading “Research Guide for Neural Architecture Search”
