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As someone who has designed neural networks destined for real-time use on mobile devices, state-of-the-art NLP models like transformers have always felt off limits.

Standard configurations for a model like BERT can easily tip the scales at over 1GB in size with more extreme versions 10 to 100 times larger than that. Recent advances in distillation and quantization, however, motivated me to reconsider my assumptions that NLP was something only accessible with multiple high-end GPUs.

Continue reading “Optimizing a HuggingFace Transformer Model for Toxic Speech Detection”

Whichever CI server you use for development, being able to access it programmatically via REST API is an essential capability for implementing all kinds of automation. In this article, you’ll learn how to utilize the TeamCity REST API using the TeamCity Kotlin REST Client.

Continue reading Continuous Integration with the TeamCity Kotlin REST Client

There are now a bunch of off-the-shelf tools for training artistic style transfer models and thousands of open source implementations. Most use a variation of the network architecture described by Johnson et al to perform fast, feed-forward stylization.

As a result, the majority of the style transfer models you find are the same size: 7MB. That’s not an unreasonably large asset to add to your application, but it’s also not insignificant.

Research suggests that neural networks are often way larger than they need to be—that many of the millions of weights they contain are insignificant and needlessly precise. So I wondered: What’s the smallest model I can create that still reliably performs style transfer?

Continue reading “Creating a 17 KB style transfer model with layer pruning and quantization”

Last month, I wrote an article that explored the nature of videos and how to use Keras, OpenCV, and ImageAI to easily run object detection code on videos and a live camera feed. If you haven’t read this article, kindly visit this link, as it will be very helpful in understanding what will be covered in this post.

The previous article walked us through installing all the Python dependencies, downloading the detection models, running the code on both video files and a live camera feed, and saving the detected video.

Continue reading “Analyze and Visualize Detected Video Objects Using Keras and ImageAI”

What is feature engineering?

Feature engineering is the use of domain knowledge to create features that make machine learning algorithms work. It’s a paramount step in the real-world application of ML.

It’s also both difficult and expensive.

Feature engineering is essentially the process of creating new input features from existing attributes to improve model performance. It’s about isolating/highlighting key information to help your algorithm “focus” on what’s important. Feature engineering takes place in both data preparation and model building.

Continue reading “Engineering Feature Engineering”

This article is for anyone that wants to learn the basics of neural networks applied to graphical content, which can be either images or videos.

As an iOS developer myself, I was initially frightened to look into anything related to machine learning. But, one day I stumbled upon a Computer Vision book called Computer Vision – Algorithms and Application by Richard Szeliski. I wouldn’t recommend it for beginners, but it was good introduction for me.

Continue reading “Exploring Convolutional Neural Networks (CNNs) from an iOS Developer’s Perspective”