Author: Fritz

NLP is a hot topic right now in the deep learning and machine learning communities, thanks to the incredible development of Transformer-based neural networks like BERT and GPT-2.

Natural language processing (NLP) gives programs and applications the ability to understand spoken and written human language.

Developing NLP applications is difficult because traditionally, computers are designed for humans to interact with them in precise, unambiguous, and highly-structured programming languages, or by using a limited number of set spoken commands.

Continue reading “Sentiment Analysis iOS Application Using Hugging Face’s Transformers Library”

It’s seems that, in the last decade, people increasingly have turned to social networks to express their opinions on everything from everyday life to politics and even businesses. This has been an important source — for businesses — to probe the market and get a clear picture of the needs and directions that influence it.

For me, the social media that seems the most active in the area of expressing opinions is probably Twitter.

Continue reading “Processing Tweets Using Natural Language and Create ML on iOS”

Machine learning has gained plenty of momentum recently, and with Google’s announcement of TensorFlow Lite, it’s never been easier to start with incorporating machine learning directly in your mobile apps. Mobile machine learning has become a highly sought after skill, and after being introduced to TensorFlow in Google’s annual keynote, I realized that it could solve real world problems, like analyzing complex CT scans to detect tumors or reading handwritten letters to the visually impaired. With that, I decided to jump in.

Continue reading “Tutorial: Troubleshooting TensorFlow Lite on Windows 10”

Following up on my earlier blogs on running edge models in Python, this fifth blog in the series of Training and running Tensorflow models will explore how to run a TensorFlow Lite image classification model in Python.

Continue reading “Running TensorFlow Lite Image Classification Models in Python”

In gradient boosting, predictions are made from an ensemble of weak learners. Unlike a random forest that creates a decision tree for each sample, in gradient boosting, trees are created one after the other. Previous trees in the model are not altered.

Results from the previous tree are used to improve the next one. In this piece, we’ll take a closer look at a gradient boosting library called CatBoost.

Continue reading “Fast Gradient Boosting with CatBoost”

In most Android devices, sensors are a vital component—we use them in many applications to perform different tasks and to improve user experiences.

They are the devices way of interacting with the outsider world, and using them we can measure motion, orientation, device position, and various environmental conditions.

In this short article, we’ll explore the different sensors we have at our disposal and how they work. Then, we’ll move to sensor fusion and see how the Android SDK classes simplify interacting with the array of device sensors.

Continue reading “3 Simple Steps to Integrate Sensors in Your Android Application”

Several weeks ago, we all watched as Redditors took on the hedge funds and the price of Gamestop’s stock (GME) rocketed up to $470 at its peak before coming back down to Earth.

During the craze, billions of dollars exchanged hands in a historic short squeeze that left millions of people asking — “do you have diamond hands?” The typical Internet forum lingo suddenly became part of our vernacular (for better or worse), as Redditors banded together to hold out for a moon shot.

Continue reading “Got Diamond Hands? Creating my first Snapchat Lens in honor of WSB”

Nothing frustrates me more than filling out a long form on my phone. The keyboard is too small and questions always feel redundant. Streamlining UX with helpful suggestions and auto-completion is a great way to reduce friction, increase conversion, and make your app feel more fluid.

Amazon realized this early, resulting in their infamous “1-click” patent. Google reports that over 12% of all emails sent are composed via their single click Smart Reply option. In this tutorial, we’ll use Apple’s Create ML tool to train our own machine learning model to anticipate a user’s intentions, automatically complete a form, and save them a click in the process.

Continue reading “Using Create ML on iOS to auto-complete forms”