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.
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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
There’s this farmer. His cows have stopped producing milk so he enlists a group of academics from the local university to help. The team arrives at the farm and the psychologists, mathematicians, physicists, and biologists start collecting pages of data, measuring and observing every move a cow makes.
One by one the academics leave telling the farmer they need time to analyze their data. At last there is one scientist left, a physicist. He walks over to the farmer, “I have a solution,” he says “assume a spherical cow…”
Face mesh provides a 3D mesh that mimics the user’s facial movements. You can add 3D masks using the face mesh which makes your lenses more interesting. In this article, we will create a mask using the material provided in the asset library. Then, we’ll attach objects to the mesh so that they can move with the user’s face movements.
If you went back in time—2 years ago, let’s say—and asked me to write an algorithm that could take an image of a hand and identify whether it’s making the symbol for a rock, paper, or scissors, I would have said, I doubt it, but give me 6 months.
Continue reading Using TensorFlow.js to Train a “Rock-Paper-Scissors” Model
Everyone knows Siri, and many people use it every day. Why? Because Siri provides a very fast and user-friendly way of interacting with an iOS device.
Convenience is not the only motivation for this type of interaction, though. The combination of speech recognition and speech synthesis feels more personal than using a touch screen. On top of that, the option for verbal communication enables visually impaired people to interact with your app.
Continue reading Speech recognition and speech synthesis on iOS with Swift
In the previous article of this series on developing Flutter applications with TensorFlow Lite, we looked at how we can develop Digit Recognizer with Flutter and TensorFlow Lite, Image Classification with Flutter and TensorFlow Lite, and Object Detection with Flutter and TensorFlow Lite.
