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”
If you’re looking for the best ChatGPT alternatives, you’re in the right place. Since launching in 2022, ChatGPT took the world by storm.
The AI chatbot, ideal for creating content, answering complex questions, and boosting productivity, introduced millions of users to a new type of AI.
Powered by large language model technology (GPT3.5 for the free version and GPT-4 for the paid version), ChatGPT is one of the world’s leading AI tools.
However, it’s far from the only generative AI solution on the market. In fact, dozens of similar solutions have emerged to help summarize content, streamline tasks, and sharpen the competitive edge of users.
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.
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.
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.
In this article, we’re going to discuss Swift 5 value and reference types and see how they’re handled.
Value types are faster to work with since they operate out of the stacks, and copying a value is cheap since it happens in a constant time. Values also help us achieve predictable behaviour and isolation, whereas reference types give shared access to memory locations and dynamic storage.
NSLinguisticTagger, which was available as far back as the iOS 5 SDK, paved the path for Apple’s announcement of their Natural Language framework at WWDC 18. Everything from language identification to lemmatization and part-of-speech tagging, all of which were present in NSLinguisticTagger, are now a part of the Natural Language framework, with an API that’s been completely redesigned in Swift.
The added benefit that the Natural Language framework has over the NSLinguisticTagger is the ability to use custom NLP models.
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