When I first saw CyreneQ’s lenses, I was immediately struck by how fun and quirky each individual one was. From bearded dragons hanging out on your face, to a literal croc on your foot, each one is joyful, unique, and ultimately shareable.
I caught up with Cyrene, who’s used Snapchat since its inception, to learn more about her inspiration. Some highlights of our conversation are below.
Frank and Xuan of Paper Triangles really enjoy and are enthusiastic about the work they do—that was one of my main takeaways from my recent conversation with them.
Their Lenses reflect that spirit—they’re often vibrant and atmospheric, with a generally light color palette that sometimes verges on something resembling neon.
I also appreciate how they use old ceramic statue heads to display their Lenses on their Creator profile—adds a sense of elegance and grandiosity that complement their aesthetic nicely.
Continue reading “Snapchat Lens Creator Spotlight: Paper Triangles”
Cross-platform mobile app development had always been a dream for both iOS and Android app developers. Even with the existence of tools like Cordova and Ionic, this dream has long remained elusive, as these tools resulted in performance that was no where near native performance.
Many argue that React Native is the best framework for the same, but ever since the launch of Flutter, this has started to change.
Continue reading “Building a Cross-Platform Image Classifier with Flutter and TensorFlow Lite”
Following what we learned about human vision, we’re now going to transfer some of that knowledge to computer vision (exciting right?!).
Very quickly, to quickly explain/recap this series, Joseph Redmon released a set of 20 lectures on computer vision in September 2018. As he’s an expert in the field, I wrote a lot of notes while going through his lectures. I am tidying my notes for my own future reference but am posting them on Medium also, to hopefully be useful for others.
Continue reading “The Ancient Secrets of Computer Vision 3 by Joseph Redmon — Condensed”
Using ML Kit, the language of a string of text can be determined. The ML Kit API supports over 100 languages. It can also identify native and romanized text from languages such as Russian and Arabic. Given a string of text, the API provides the most likely languages as well as the confidence level. Let’s look at how that can be done.
Continue reading “Identify the language of text with ML Kit on Android”
Phased Long Short Term Memory is an improvement on the well known Long Short Term Memory units. Its main advantage is its ability to deal with data that do not follow a simple sequence and data with long timesteps.
Phased LSTM differs from LSTM by the possession of an additional gate called the time gate. The phased version is very efficient and performs better than basic LSTMs, even when given fewer data.
For the last few months, I’ve been working on my own machine learning model to predict what kind of coffee (hot or iced) I should drink on any given day, based on a variety of factors.
I’ve become very interested in machine learning over the past year, and so I wanted to build something to help me learn—so I built an app (or 2)!
Many organizations are leveraging machine learning to analyze large customer databases and identify customer loyalty; or, perhaps more importantly, which customers are at the highest risk of churning.
Accurate prediction of churn is extremely valuable and, if the right steps are taken to retain customers at risk, businesses can lift LTV across an entire portfolio.
Continue reading “Machine Learning Techniques for Predicting Customer Loyalty”
When developing in any programming language or framework, it’s usually a good idea to learn and follow established best practices. This is even more crucial in Flutter given the way widgets are built and re-built.
Following established best practices in Flutter is also particularly important for a couple of specific reasons: code readability and application performance.
In this post, we’ll cover some of the best practices in Flutter that can help on both of these fronts.
Whenever someone asks me to explain what mobile machine learning is, I instinctively bring up Snapchat as a core example.
In 2015, the incredibly popular social content platform added Lenses to their mobile app—if you’ve ever played with Snapchat, you know these well. They’re essentially augmented reality (AR) filters that give you big strange teeth, turn your face into an alpaca, or trigger digital brand-based experiences.
