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Mobile devices are limited in their resources compared to PCs. Some calculations can be executed on such limited-resources devices. For example, doing some calculations on an image or a couple of images. As the number of images increases, however, the device may run out of RAM.

If a deep neural network, for example, is to be trained on a large dataset with thousands of images, mobile devices are not a great option for training; rather, we need to us a machine with much more resources.

Continue reading Uploading images from Android to a Python-based Flask server

In this post, we’re going to dive deep into one of the easiest and most interpretable supervised learning algorithm — decision trees. Decision tree algorithms can be used for both classification and regression. We’ll be discussing how the algorithm works, it’s induction, parameters that define it’s structure, and it’s advantages and limitations.

Continue reading Understanding the Mathematics Behind Decision Trees

In this post, we’re going to dive deep into one of the most influential unsupervised learning algorithms—k-means clustering. K-means clustering is one of the simplest and most popular unsupervised machine learning algorithms, and we’ll be discussing how the algorithm works, distance and accuracy metrics, and a lot more.

Unsupervised learning is a type of self-organized learning that aids us in discovering patterns in our data related to various features. It is one of the three main categories of machine learning, along with supervised and reinforcement learning.

Continue reading Understanding the Mathematics behind K-Means Clustering

When you think about a Xamarin (or any cross-platform) application, performance is the most important consideration. Optimizing performance in a native app is a bit easier than with a cross-platform application, as there are hundreds of blogs and books and other resources to help you along the way. But there are less of these resources when it comes to apps developed with cross-platforms like Xamarin.

Continue reading Techniques for Improving Performance in a Xamarin.Forms Application

Research in computer vision has been booming over the past few years, thanks to advances in deep learning, increases in computing storage, and the explosion of big visual datasets. Every day, there are more computer vision applications in fields as diverse as autonomous vehicles, healthcare, retail, energy, linguistics, and more.

In this article, I’ll present the 5 major trends that have dominated computer vision research in 2018. An exhaustive review is impossible, so I’ll only share some of the accomplishments in the field that have most impressed me.

Continue reading “The 5 Trends Dominating Computer Vision”

A few months back, as I was browsing through some fascinating projects on Augmented Reality, I came across an extremely intriguing project, which was one of the most artistic applications of AR I could think of. The final effects of the app looked something like this:

Cool isn’t it!? Go ahead and try out the awesome app for yourself.

Continue reading Using ARCore and Unity3D to Draw Lines in Augmented Reality

One of the most crucial aspects of machine learning is understanding the mathematics & statistics behind it. In my journey to becoming a data scientist, I wanted to master not only the theoretical aspects of math & stats but also understand how I could apply them to my area of work.

There is an ever-increasing number of machine learning algorithms, and this post is going to focus on one of my favorites — the Naive Bayes algorithm. Specifically, I’m going to break this exploration into two parts—the first part is going to broadly cover the Naive Bayes algorithm and how it can be applied in text classification. And the second part of it is going to focus on building a REST API from the model we create in Part I. So stay tuned and enjoy!

Continue reading Understanding Naive Bayes & its applications in text classification (Part I)

The genetic algorithm (GA) is a biologically-inspired optimization algorithm. It has in recent years gained importance, as it’s simple while also solving complex problems like travel route optimization, training machine learning algorithms, working with single and multi-objective problems, game playing, and more.

Deep neural networks are inspired by the idea of how the biological brain works. It’s a universal function approximator, which is capable of simulating any function, and is now used to solve the most complex problems in machine learning. What’s more, they’re able to work with all types of data (images, audio, video, and text).

Continue reading Train Neural Networks Using a Genetic Algorithm in Python with PyGAD

If you’re even remotely plugged into the tech world, you’d have been hard-pressed to miss a couple viral summer trends, both involving AI-powered photo transformations.

Here’s the gist. Snapchat caught fire and soared past all Q2 estimates, in large part because of their rollout out of popular gender-swap and baby-face Lenses. And soon after, FaceApp took the internet by storm when its old-age filter went viral. This has led to millions of users, including prominent celebrities, showing off what they’d look like in 40 years or as members of a different gender.

Continue reading Snapchat, FaceApp, and the necessary lessons of data privacy with mobile machine learning

Very rarely do we find mobile apps that have no connection to a backend of any sort. There are many services and platforms like Azure or AWS, but one Google product stands out as well: Firebase. It’s easy to use, scalable and—most importantly—free. In this tutorial, we’ll explore how to connect our Flutter mobile app to Firebase.

Continue reading Using Firebase’s Cloud Firestore in Flutter