Category: Articles

Tween Manager is used in making custom animations within Lens Studio. This helps you make your lenses more engaging and captivating. You also don’t need to write any code. In this article, you will learn how the different types of tweens works.

Continue reading “Tween Manager in Lens Studio”

In our post from about a month ago, we compared two of the major on-device text recognition SDKs on iOS: Firebase’s ML Kit & Tesseract OCR. The results were pretty one-sided, as ML Kit outclassed Tesseract on many of the predictions and showed much better accuracy even when it failed.

Continue reading “Comparing Firebase ML Kit’s Text Recognition on Android & iOS”

Mobile ML, Retail, and E-Commerce

When it comes to retail and e-commerce, the AI revolution is already here. From AI-enhanced search and at-home product demos to clothing/accessory try-on applications, machine learning is increasingly baked into shopping experiences, both in-store and online.

Continue reading “Mobile Machine Learning in E-commerce and Retail: Visual Search [Ebook Sample]”

TensorFlow was the new kid on the block when it was introduced in 2015, and since then, it’s become one of the most used deep learning framework.

I jumped on the train a few months after the first release and began my journey into deep learning during my master’s thesis. It took a while to get used to the computation graph and session model, but since then I’ve got my head around most of the quirks and twists.

Continue reading “TensorFlow Quick Tips for 2024”

Why is Sentiment Analysis Important?

Sentiment analysis has become a hot topic in the fields of Natural Language Processing and machine learning. Sentiment analysis is the process of examining a piece of text for opinions and feelings.

There are innumerable real-life use cases for sentiment analysis that include understanding how consumers feel about a product or service, looking for signs of depression, or to see how people respond to certain ad and political campaigns.

Continue reading “Training a Core ML Model for Sentiment Analysis”

In this post, I’ll be covering how CNNs are implemented efficiently in deep learning frameworks like TensorFlow and PyTorch.

When I was writing my own framework for Binarized CNNs in C++, I expected it to work as fast as PyTorch. The result — my Conv implementation was 100x slower than PyTorch. I even compared the number of FLOPs of my code with PyTorch. But the FLOP count was same.

Continue reading “The Engineering Behind Convolutions”

In the previous post of my Beginner’s Guide to NativeScript series, we kicked things off by focusing on the high-level benefits of using NativeScript, along with a simple demo app to get us started.

If you haven’t read this introduction, and you’re unfamiliar with NativeScript, I’d suggest taking a look.

Continue reading “Beginner’s Guide to NativeScript: Creating a Multi-page Application”

Ready to learn how to train your AI in less than a day without writing a single line of code? If you’re sitting on a pile of data that you want to make useful, but AI seems too daunting of a task to get started, then this article is for you.

Or if you have experienced the trenches of AI and are ready to make the model training process a whole lot simpler and faster, you’ll also find this article eye-opening.

Off-the-shelf algorithms have gotten so good that they’ve ushered in a new paradigm of accessibility in machine learning.

Continue reading “AI is More Accessible Than You Know”