In this post, we’re going to employ one simple natural language processing (NLP) algorithm known as bag-of-words to classify messages as ham or spam. Using bag of words and feature engineering related to NLP, we’ll get hands-on experience on a small dataset for SMS classification.
So, what are we waiting for?
Apple has recently been pushing the envelope with regards to Artificial Intelligence, and WWDC 2020 was no different.
From machine and deep learning to computer vision and natural language processing, Apple’s introduced a slew of enhancements and improvements across their built-in frameworks that help mobile application developers build better AI-powered iOS apps.
PencilKit, a drawing framework that was introduced in iOS 13, was also powered with machine learning this year.
Continue reading Advancements in Artificial Intelligence in iOS 14
I’ve never quite attended an event like Lens Fest. Not just because it was entirely virtual, but largely because of the community of people it was celebrating — AR developers, graphic designers, 3D artists, software engineers, ML engineers, and a wide range of other people from around the world who, put simply, create really cool stuff. I was blown away by all the amazing experiences people from all walks of life are building with Lens Studio.
In this piece, we’ll look at some of the top open source machine learning projects in 2019, as ranked by MyBridge.
This project is an implementation of the SV2TTS paper with a vocoder that works in real-time. Using this repo, one is able to clone a voice in 5 seconds to generate arbitrary speech in real-time.
Continue reading 2019’s Top Open Source Machine Learning Projects
Having had the privilege of compiling a wide range of articles exploring state-of-art machine and deep learning research in 2019 (you can find many of them here), I wanted to take a moment to highlight the ones that I found most interesting. I’ll also share links to their code implementations so that you can try your hands at them.
Continue reading Top Machine and Deep Learning Research Papers
Most of the attention, when it comes to machine learning or deep learning models, is given to computer vision or natural language sub-domain problems.
However, there’s an ever-increasing need to process audio data, with emerging advancements in technologies like Google Home and Alexa that extract information from voice signals. As such, working with audio data has become a new trend and area of study.
Continue reading Working with Audio Data for Machine Learning in Python
TLDR: OpenCV’s camera doesn’t handle a mobile device’s portrait mode well by default. Grab the code below and drop it into CameraBridgeViewBase to utilize the OpenCV rear and front facing Camera in full screen portrait orientation.
Jump to:
Even with all of the recent developments in Android’s ARCore, there are plenty of reasons you might need OpenCV in your mobile Augmented Reality project. With image processing, machine learning, object detection, optical flow, and numerous other features — the library does a lot, and it isn’t bound to just one platform, meaning that with minimal changes you can port your code to iOS, Unity, Python, and more.
Continue reading Working with the OpenCV Camera for Android: Rotating, Orienting, and Scaling
In this third installment of our Flutter series, we’ll look at how you can use native device features. Specifically, the features we’ll look at are designed for working with a device’s camera and gallery.
By the end of this piece, you’ll be able to build an app that takes images via the camera or gallery and stores it on the device. We’ll also see how you can use the Provider package to store images in a way that enables you to send them to a backend server.
So you’ve got your Keras model set up, and it can do everything you want it to do. But how do you get it onto an iOS device? Thanks to Apple’s Core ML library, this process is painless and can be done in less than 10 lines of code. Better yet, once you write the code I’ll show you below, there’s very little you’ll have to change for the next time you need to convert a model. Here’s a link to the GitHub repo:
Continue reading Using coremltools to Convert a Keras Model to Core ML for iOS
User-generated content has increased significantly in the recent past. Much of this content is text-based, generated mainly via online forums and social media platforms, an will often contain users’ opinions about organizations or hot-button issues.
Businesses exist to provide goods and/or services, which means that communication and relationships with customers are crucial elements of their success. Analyzing customer feedback—either customer reviews or complaints—shared on online or social medium platforms can provide key insights necessary to optimize customer service. In fact, there a lot of statistics that suggest this kind of analysis via user-generated content is a key part of any brand strategy.
Continue reading Using Aspect-Based Sentiment Analysis to Understand User-Generated Content
