Android’s Jetpack Compose is a modern toolkit for building native UIs. It’s simplified and is designed to accelerate UI development on Android while requiring less code
With Jetpack compose you don’t need to write an XML —calling a Jetpack Compose function will do the magic and create the element that you want.
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Using Google’s ML Kit one can build an application that can be consumed in more than 50 languages. For example, you can have users select the language they prefer and translate the app’s content into that language.
The translation is quite fast because it happens on the user’s device. The ML Kit translation models are built to translate to and from English.
Those of you that have used meditation or breathing apps before might be familiar with this situation: You’re supposed to follow a simple breathing pattern. Inhale for four seconds, hold, exhale for four seconds. Sounds easy, but it can actually be pretty hard.
Soon after starting the exercise your mind starts drifting and you forget about that deep breath. But while you’re distracted, the exercise in your app keeps going until you’ve added another day to your mindfulness streak.
Once your machine learning model is ready, you have to deploy it to a device. One of the ways that can be done is by shipping the model with the application. A challenge with this method is that whenever your model changes, you will need to ship a new APK to the app stores.
Obviously, this takes a long time because every app update needs to be verified by the app store. Now, imagine if it was possible to update the model over the air without the need to ship a new application. In this article, we will see how that can be done using Firebase Machine Learning.
Continue reading “Custom TensorFlow Lite model on Android using Firebase ML”
Sufee Yama is changing the way we see reality. Whether she’s creating amazing commercial AR integration, throwing a quarantine dance party, or leading workshops for other curious, multi-talented artists, I can only sit back in awe.
Continue reading “Snapchat Lens Creator Spotlight: Sufee Yama”
Ever wonder how paint colors are named? “Princess ivory”, “Bull cream.” And what about “Keras red”? It turns out that people are making a living naming those colors.
In this post, I’m going to show you how to build a simple deep learning model to do something similar — give the model a color name as input, and have the model propose the name of the color.
This post is beginner friendly. I will introduce you to the basic concepts of processing text data with deep learning.
Continue reading “Training a Keras model to generate colors”
iOS’s Natural Language framework allows us to analyze language and to perform language-specific tasks like script identification, tokenization, lemmatization, part-of-speech tagging, and named entity recognition.
In this introduction tutorial, we will discover this framework’s capabilities by looking at 4 common and essential techniques:
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The education industry has embraced recent technological advancements with open arms with the inclusion of online modules, topic-based discussion forums, and the option of communicating with lecturers after hours.
While these developments have made the learning process more comprehensive and simplified for students, there’s still a lot of untapped potential in the industry.
Machine learning is blazing paths for new, more personalized learning experiences that have the potential to improve student engagement, create clearer communication channels between lecturers and students, and to develop less biased grading systems.
Continue reading “4 Ways Machine Learning Is Shaping the Future of Education”
At the recent TensorFlow Dev Summit, Google announced upcoming support on the TensorFlow platform for Swift. Their goal is to make it easier to use machine learning libraries and help catch more mistakes before running ML code.
Swift for TensorFlow — and some new Swift extensions planned for the upcoming Swift 4.2 release — will let you execute arbitrary Python code including scientific packages like NumPy, making it simple to port existing TensorFlow Python code to Swift.
The Levenshtein distance is a text similarity metric that measures the distance between 2 words. It has a number of applications, including text autocompletion and autocorrection.
For either of these use cases, the word entered by a user is compared to words in a dictionary to find the closest match, at which point a suggestion(s) is made. The dictionary may contain thousands of words, and thus the response of the application for comparing 2 words will likely take a few milliseconds.
Continue reading “Optimizing the Levenshtein Distance for Measuring Text Similarity”
