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With over 2 billion active Android devices and over 1 billion active iOS users, the mobile market provides the most engaging and profitable market to build and sell new digital solutions. There are less than 4 million unique applications for each of these operating systems, with most of them performing the same or related functions.

However, the arrival of cloud-based and device-based artificial intelligence tools provides a unique opportunity to recreate the mobile experience for existing apps, as well as build entirely new mobile apps that can only be possible through the use of AI-powered tools.

Continue reading “5 App Ideas to Unleash the Power of Mobile Machine Learning”

In machine learning, overfitting is having your machine learning model study the data so well that the model somehow memorizes the data and loses the ability to predict correctly given an unseen situation.

Continue reading “Bias-Variance Tradeoff to Avoid Under/Overfitting”

In our last newsletter roundup, we looked digests for mobile development, and we covered it all: iOS, Android, React Native, and Cross-Platform. Again, my sincerest thanks to Heartbeat All-Star Zain Sajjad, whose help in putting these lists together has been invaluable.

For the last installation of our newsletter journey, let’s take a look at some of the premier newsletters for data science and analytics.

Continue reading “Newsletters for Data Science & Analytics”

In our last newsletter roundup, we looked at the expansive landscape of AI and machine learning newsletters. Luckily for me (and Zain Sajjad, who was a huge help in putting this list together) the demarcations with mobile development newsletters are much more straightforward.

That doesn’t mean there’s any less variety or quality in the content that’s being sent all around the world every week (or every other week). I had a lot of fun researching these excellent newsletters, and I know there’s something in here that will capture your interest. So let’s jump right in!

Continue reading “Newsletters for Mobile Development”

As a developer, it’s crucial for your business to have access to your user’s details. Be it to monitor their usage patterns, send promotional campaigns, notify them of critical updates to your services, or for additional cross-selling of new services.

One effective technique to gather user data is via authentication. Or, put simply, a login system that lets your app know when a particular user is using the app.

Implementing a secure authentication system of this kind from the ground up might require weeks of effort, along with you having to learn a server-side framework to build the backend for the same.

Continue reading “Implementing email and password-based authentication on Android using Firebase”

This is part 3 of an ongoing series on language models, starting with defining neural machine translation and exploring the transformer model architecture, and then implementing GPT-2 from scratch.

Specifically, in the first part of the series, we implemented a transformer model from scratch, talked about language models in general, and also created a Neural Machine Translator.

Continue reading “Exploring Language Models for Neural Machine Translation (Part Three): Generating Text with Hugging Face”

A few weeks ago I looked at the speed of Core ML models running on various Apple devices. Apple’s new A12 Bionic chip paired with Core ML 2 made the latest generation of iPhones and iPads more than 10x faster than previous generations.

While faster is usually better, large differences in performance across devices make it hard for developers to keep user experience consistent. Apple’s (relatively) small product family and aggressive upgrade strategy mitigates this problem to some degree.

Continue reading “Benchmarking TensorFlow Mobile on Android devices in production”

Machine learning and AI are having more and more influence on mobile tech nowadays. Hardware is becoming more and more AI-capable, and machine learning and AI methods are being integrated in mobile apps to optimize and enhance user experiences.

In this tutorial, we’re going to apply machine learning methods provided by the TensorFlow Lite library for the purpose of image classification in a Flutter app. Flutter—as introduced and acknowledged by Google—has a tendency to produce and support libraries related to machine learning.

Continue reading “Dog Breed Classification on Mobile with Flutter and TensorFlow Lite”