With the recent advancements in artificial intelligence (AI), the music industry has had a massive change, lowering the entry barriers for new music creators, producers, content creators, and composers.
These advancements mean you no longer need to master playing instruments and navigate complex music production software to create high-quality music.
Continue reading “8 Best AI Music Generators for 2024 (Expert Reviews)”
Building a professional website can have a painstaking learning curve.
While the traditional way offers complete control over how you want your website to be, it demands a significant investment of time, effort, and technical know-how in return.
However, in the past 5 years, the speed of integrating artificial intelligence into web development has greatly accelerated, making the process faster, more efficient, and accessible to everyone.
It goes without saying that if you’re not using AI to help build your website today, you’re wasting hours of precious time that could be better spent on more important aspects.
Continue reading “How to Build a Website With AI (2024 Guide)”
For Oregon-based Gerard Killeen, the interaction of the user and his lenses is paramount. The Lens Creator, who also writes poetry among other endeavors, strives to bring joy and introspection to his work.
Though some of his lenses lean toward the macabre (beware, Arachnophobs), many are intriguing, funny, and totally imaginative. I caught up with Killeen to learn more about his process.
Continue reading “Snapchat Lens Creator Spotlight: Ger Killeen”
If you’re looking for an AI-based tool that helps you generate blog content, marketing copy, or social media posts, Copy.ai can be a good option.
As the name suggests, Copy.ai is used to generate online copy. It is built on GPT-3 and offers professional features such as teamspaces, projects, and workflows.
While it is designed to generate all types of content, it is largely geared towards marketers. For example, if you’re looking for an ad copy for an online campaign, Copy.ai can be the right tool. And since it has a free version, there’s a lot you can explore without paying.
Continue reading “Copy.ai Review 2024 – Understanding AI Copywriting”
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”
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.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.
