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”
Finding old black and white photos and imagining what people, landscapes, and objects looked like in color has probably already crossed the minds of many.
While it is possible to manually colorize black and white photos using image editing and retouching software such as Photoshop, the manipulation, which is only available to insiders and professional image editors, can be time-consuming and tedious.
Continue reading “Colorizing Images in an iOS App Using DeOldify and a Flask API”
Depression is among the most prominent mental illnesses, affecting more than 300 million people globally, according to the World Health Organization (WHO).
Beyond the potentially devastating personal and social effects of depression, the economic costs related to this mental health issue have substantially increased in recent years. In fact, it’s currently estimated to cost the global economy over $1 trillion USD each year.
Continue reading “Predicting Depression from Routine Survey Data using Keras”
Since we founded Fritz AI in late 2017, we’ve seen tremendous growth in the Boston-area tech scene. What was once a city whose tech economy centered on academia, biotech, and robotics, Boston has flourished into one of the most attractive locations for all kinds of tech start-ups, early- and late-stage alike.
Continue reading “Fritz AI Named a 2020 Boston “Startup to Watch” by Built In Boston”
Let’s say you want to make an iOS app that can recognize audio signals in real time, followed by the triggering of an action. From a global point of view, we’d need to explore the crossroads of machine learning and and mobile development.
Continue reading “Building a Sound Classification iOS Application using AI”
