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.
Following up on my blog post on training a TensorFlow Lite model with AutoML Vision Edge, this blog post aims to teach you how to load that .tflite model into an Android app and run inference (i.e. predictions) on it.
If you haven’t read that previous blog on training your own TensorFlow Lite model, I highly suggest that you do so before proceeding with this blog:
Continue reading “Loading and running a quantized TensorFlow Lite model on Android”
Core ML is an interesting means for adding a pre-trained machine learning model to your app. But one question that nagged me after trying Core ML was—How can I possibly train my own model and integrate it in my apps using Core ML?
Well, after doing some homework, a learned a lot about the possibilities of achieving this. To be honest, all the methods require you to understand and know your math really well! While I was on this roller coaster ride, I came across Custom Vision.
What a relief for developers looking to jump straight into training their own machine learning models. With the help of Custom Vision, developers can easily manifest their machine learning ideas into real mobile apps without diving too deeply into the machine learning waters.
Continue reading “Using Core ML and Custom Vision to Build a Real-Time Hand Sign Detector in iOS”
The wave of AI and machine learning is happening just as the dominance of mobile is becoming set in stone. As mobile devices become more ubiquitous and powerful, a lot of the machine learning tasks we think of as requiring months of high-powered compute time will be able to happen right on your phone.
This post will outline why edge devices are increasingly important, and how machine learning works with them.
Continue reading “Machine Learning models on the edge: mobile and IoT”
