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The definition of an edge device can vary greatly from application to application, and it includes devices ranging from smartwatches to self-driving cars and everything in between. Currently, the edge devices with the largest numbers, which also have a connection to a network, is likely the smartphone.

There are increasingly a lot of other devices with small MCU’s (microcontrollers) that aren’t connected to any network which can be used for applications like intelligent sprinkler system for home garden.

Continue reading Machine Learning at the Edge — μML

In previous tutorials in this series, we began the development of an Android chat application that allows users to do the following:

Register and verify user email addresses; login to the system Fetch chat conversations from other users Send and receive messages within the conversations

Continue reading “Instant Notifications for New Messages in an Android Chat Application”

In this article, we’re going to introduce self-organizing maps. We assume the reader has prior experience with neural networks.

Self-organizing maps are a class of unsupervised learning neural networks used for feature detection. They’re used to produce a low-dimension space of training samples. Therefore, they’re used for dimensionality reduction.

Continue reading Introduction to Self-Organizing Maps (SOMs)

Deep neural networks (DNNs) can have tens of thousands of parameters, and in some cases, maybe even millions. This huge number of parameters gives the network a huge amount of freedom and the flexibility to fit a high degree of complexity.

This flexibility is only good up to a certain level. When this level is crossed, the term overfitting is brought to the table.

Continue reading 5 TensorFlow techniques to eliminate overfitting in DNNs

MessagingCenter is one of the most important APIs that you should be aware of while working with Xamarin.Forms and MVVM. MessagingCenter includes a simple messaging service to send and receive messages.

Please refer to this amazing document by Microsoft, which will run you through some basics, some of which I’ve included in this post in detail:

Continue reading Xamarin.Forms MessagingCenter

One week into my first year physics course at the University of Michigan, a professor assigned a problem set that required simulating some many-body system. It was due Friday. That was the week I learned my first programming language, Matlab.

This is how I’ve picked up bits and pieces of a dozen or so languages over the past decade. Besides an introductory CS class taught with C++ and a Java-based database class in graduate school, I never had any formal training in software engineering. For me, coding was a way to finish my homework, analyze data to answer a question, or turn an idea in my head into something real.

Continue reading Why data scientists should start learning Swift

In this article, we’ll train a model that is able to label cat and dog images. The images used for this project can be found here and here. Of course, you don’t have to use cats and dogs; the process is the same irrespective of your images. Before we get too far into this, you’ll first need to create an account at Fritz AI.

Continue reading Image Segmentation with Fritz AI Studio

Geolocation as an API has different methods that can be used in a web application. But it’s also a powerful API for mobile development. Ride share mobile apps like Uber, map app like Google Maps, and location features implemented in apps like Instagram depend on using this API. React Native takes advantage of this API and its available methods by extending the Geolocation Web specification.

Continue reading How to use the Geolocation API in a React Native app

In this article, we’ll build a simple neural network using Keras. We’ll assume you have prior knowledge of machine learning packages such as scikit-learn and other scientific packages such as Pandas and Numpy.

Continue reading Introduction to Deep Learning with Keras

In the last piece in this series on developing with Flutter, we looked at how we can implement face detection using ML Kit, which belongs to the Firebase family.

In this 8th installment of the series, we’ll keep working with ML Kit, this time focusing on scanning barcodes from an image.

Continue reading Scan Barcodes in Flutter Using Firebase’s ML Kit