Federated learning is a new way of training a machine learning using distributed data that is not centralized in a server. It works by training a generic (shared) model with a given user’s private data, without having direct access to such data.
For a deeper dive into how this works, I’d encourage you to check out my previous blog post, which provides a high-level overview, as well as an in depth look at Google’s research.
If you’ve ever considered working with the new Core ML framework in iOS, you might have seen a demo video like this:
For a major launch such as this, Apple normally holds a press conference in their Cupertino headquarters. However, due to growing health concerns and widespread retail shutdowns, the company opted instead for an online-only launch for their latest device.
Sporting a powerful 8-core processor and LiDAR scanner, the iPad Pro looks more like a computer replacement than ever before.
Continue reading “The New iPad Pro’s LiDAR Sensor Changes the Augmented Reality Game”
If you’re not a native English speaker and are struggling with writing a professional email, Quillbot can come to your rescue.
It’s a rewriting or paraphrasing tool that can give a neat twist to your text. And not just that, it can check for grammar and plagiarism as well.
It’s a great tool for students, editors, and researchers. It can also be used by writers. As a writer, you must have struggled at least once to find the right word in your article, blog post, or any other text.
Continue reading Quillbot AI Review (2024): Pros, Cons and Alternatives
Maybe you want to get a good pic for a job interview. Maybe you want your photo to be on this year’s Christmas cards.
Or maybe you want a social media display pic that’s totally lit – whatever your game is, you can up it with the best AI headshot generator. Get professional level photos sitting in your home in no time.
I tested some of the best AI headshot generators and compared their outputs. I aimed to find the absolute best headshot generator that creates high-quality professional quality photorealistic images in no time.
Continue reading “7 Best AI Headshot Generators in 2024 (Free And Paid Options)”
As we’ve been working through this series introducing SwiftUI, we’ve so far made an app that allows us to view our team from a list and dive into specific members’ detailed profiles.
However, all our users can do with the app is view dynamic data in a very static UI. The only interaction we’ve provided is the navigation into a team member’s detailed profile.
Continue reading SwiftUI: Interactivity, State Bindings, & Simple Animations
As we make progress in the era of edge computing, the demand for machine learning on mobile and edge devices seems to be increasing quite rapidly.
ML-enabled services such as recommendation engines, image and speech recognition, and natural language processing on the edge (to name a few) are growing, as is the need for processing large amounts of data with reduced latency.
Continue reading “MACE: Deep learning optimized for mobile and edge devices”
A few months ago, Snapchat released a new feature where AI developers and creatives can easily integrate their own custom lenses using machine learning models — one of these features is the ability to track labeled objects on the view and project fun and immersive AR effects. We’ll cover this ML task in this article.
Continue reading “Build a Snapchat Lens to Welcome New Texans Using Fritz AI”
I’m a software engineer by trade. But I seem to keep dipping my toes into the machine learning and data science communities. There are some awesome libraries out there, but I admit I’ve had a difficult time wrapping my head around many of them.
Surprisingly, it’s not the math. It’s actually the code itself that I find myself getting stuck on. It’s understandable. Tensor algebra is hard enough without needing to worry about style guides and code reviews.
Continue reading “How great data scientists can write better code”
Machine learning unlocks a wide range of possibilities when it comes to AR development—nobody has proved that more than Snapchat, whose immersive augmented reality Lenses work so well in large part because of the ML models running underneath them (things like face tracking, background segmentation, and more).
With SnapML, though, Snapchat has opened up this expansive capability to its community of Lens Creators, allowing them to implement custom machine learning models in their Lenses.
Continue reading “Demystifying common use cases for SnapML in Lens Studio”
