This blog is the fourth one in my series on training and running Tensorflow models in a Python environment. If you haven’t read my earlier blogs centered on AutoML and machine learning on edge devices, I’d suggest that you do so before continuing with this post.
Continue reading “Using Google Cloud AutoML Edge Object Detection Models in Python”
This is the ninth post in my series around training and running Cloud AutoML models on the edge. This post follows up from the post earlier on training a multi-label image classification model and covers how to run the trained model in a python environment.
Continue reading “Using Google Cloud AutoML Multi-Label Image Classification Models in Python”
When building and optimizing machine learning models for iOS deployment, there are a number of unique factors to consider. From resource constraints and optimizing inference speed, to model conversion and accessing advanced functionality, there can be quite a bit of legwork to get an ML model working effectively inside an iOS app.
Continue reading “Machine Learning on iOS: Model management and optimization”
In recent years, neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision.
While many of the face, object, landmark, logo, and text recognition and detection technologies are provided for Internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users anytime, anywhere, regardless of Internet connection.
This post is a part of a series about feature engineering techniques for machine learning with Python.
Hey again! In this post, we’re going to explore feature scaling transformations for feature engineering.
Let’s start with feature magnitude. Frequently, our dataset contains features that highly vary in scales, and that’s a problem for some algorithms that rely on distance.
Continue reading “Hands-on with Feature Engineering Techniques: Feature Scaling”
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 and image classification with Flutter and TensorFlow Lite.
In the third article of this series, we’ll keep working with TensorFlow Lite, this time focusing on implementing object detection. The application we are going to build will be able to recognize objects presented in an image.
Continue reading “Object Detection with Flutter and TensorFlow Lite”
Pam Taylor is just 16, but already making waves in the AR world. A natural creative, she’s made lenses based on breakfast cereal, makeup trends, and dream vacations.
I was able to catch up with the well-spoken, talented creator who’s already an official Lens Studio Creator and a soon-to-be AR megastar.
Continue reading “Snapchat Lens Creator Spotlight: Pam Taylor”
During WWDC 2019, Create ML got its own independent identity. It’s like an origin story of some sort, giving way to a whole new range of capabilities. Create ML is now a separate application that’s shipped with Xcode 11.
Apple once again, through Create ML, showcases how serious they are about machine learning on mobile. Their recent advancements with the Create ML framework are a big boon for mobile machine learning developers, given that these improvements allow them to easily create their own models without much machine learning expertise.
Continue reading “Sound Classification on iOS Using Core ML 3 and Create ML”
OpenCV is an open source computer vision and machine learning library. It has 2500+ optimized algorithms—a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has many interfaces, including Python, Java, C++, and Matlab.
Here, we’re gonna tackle the Python interface.
Continue reading “OpenCV-Python Cheat Sheet: From Importing Images to Face Detection”
Mobile machine learning is still, relatively speaking, in its infancy. As such, new developer tools, frameworks, and environments are regularly in flux.
It can be hard to keep up with the rapid advancements happening at this emerging intersection.
Continue reading “Mobile Machine Learning: Development Tools and Frameworks”
