Author: Fritz

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”

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:

Continue reading “4 Techniques You Must Know for Natural Language Processing on iOS”

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.

Continue reading “Introduction to TensorFlow Computation Graphs: Simulating TensorFlow Execution in 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”

The least-squares method for classification is based on linearly separating 2 or more classes. In this article, I’m going to show you how to create a Python program to classify images with digits from 0–9 using only NumPy and PIL.

Continue reading “Using least squares-based classification to detect digits”

Sometimes you might want to change part of your lenses and replace them with an image or object or even an effect. Segmentation allows you to do that while using segmentation textures.

For instance, you can change the background by adding the texture you want or changing the user’s hair by adding color or a texture to it.

Types of segmentation textures include portrait background, portrait hair, portrait shoulder, portrait face, portrait head, sky, and body. We will look at what they do later on in this article.

Continue reading “Segmentation Textures in Lens Studio”

So you came here—let me guess—it’s either you’re in a data science competition and you read somewhere about how winners of most competitions win with ensembles, or you’re just a curious data scientist who wants to learn about ensembles.

Either way, understanding how ensembles work is a very important knowledge and as data scientists and machine learning engineers, you should be able to employ the skills behind them.

Research has shown that a majority of the time, ensembles will outperform a single model, and it’s the recommended technique for maximizing accuracy or reducing errors in a machine learning model.

Continue reading “Ensemble Learning Techniques Demystified”