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We’ve all heard the saying A picture is worth a thousand words. But is a tarnished image with gaping holes or splotches or blurs worth a few hundred? What if you just found an age-old photograph of your grandparents’ wedding, but the surface was so worn that you could barely make out their faces. Or perhaps you got photobombed in what would otherwise have been the perfect picture. Or maybe you’re like me and are wondering why hasn’t anyone integrated an option in a smartphone camera app to remove unwanted objects from images?

Continue reading Guide to Image Inpainting: Using machine learning to edit and correct defects in photos

🙀 Jetson Wuh? What Is it?

The NVIDIA Jetson Nano is, essentially, an amazing little computer. I’d describe it as a lot like a Raspberry Pi, except it has a GPU.

Combine that with its low $99 USD price tag, and AI/ML enthusiasts see a cheap device that can go from desktop to edge with perfect parity.

An ideal machine for AI in the wild!

Continue reading “Getting Started with the Jetson Nano”

Just recently, the 5th stable version of the React Navigation library was released. Without a doubt, it’s one of the most popular navigation solutions in React Native apps that also has support for Expo.

Recently, it underwent some core changes. Specifically, how you used to define routes up until react-navigation version 4.x.x has some major changes.

Continue reading Getting Started with Stack Navigator Using react-navigation 5 in React Native and Expo Apps

Everyone knows that Swift is an open-source language from Apple and is available on macOS machines. Some of you might even know that Swift can be compiled on iPadOS.

But did you know that Swift is also available on Ubuntu? If yes, have you ever tried it?

Let’s try this out here—this post will walk you through compiling a Swift file on Ubuntu.

Continue reading Getting Started with Swift on Ubuntu

In this tutorial, we’re going to talk about a type of unsupervised learning model known as Boltzmann machines. We assume the reader is well-versed in machine learning and deep learning. We’ll use PyTorch to build a simple model using restricted Boltzmann machines. This model will predict whether or not a user will like a movie.

A Boltzmann machine defines a probability distribution over binary-valued patterns. What makes Boltzmann machine models different from other deep learning models is that they’re undirected and don’t have an output layer. The other key difference is that all the hidden and visible nodes are all connected with each other. Due to this interconnection, Boltzmann machines can generate data on their own. As such, it can be classified as a generative deep learning model.

Continue reading Introduction to Restricted Boltzmann Machines Using PyTorch

But there was one major issue with Prisma. It had to be run through JVM and had memory issues. The Prisma Framework is rewritten in Rust, and it addresses the issues from Prisma 1.

Prisma 1 also required a server in front of your backend server, while with the Prisma Framework, the query engine is now a bundled executable that’s run alongside the backend on the same server.

Continue reading Getting Started with the Prisma Framework (formerly Prisma 2) in React Native

In my previous article, we had a detailed discussion on the one-shot learning problem and how various algorithms tackled it in order to determine which approach was the most effective, and we settled on siamese networks. I’d encourage you to check it out:

But is this enough?Not quite! We need some hands-on-experience to actually learn how implement it. And what could be more relatable these days than facial recognition, which just happens to be a perfect use case for one-shot learning?

Continue reading One-Shot Learning (Part 2/2): Facial Recognition Using a Siamese Network

This post is a part of a series about feature engineering techniques for machine learning with python.

You can check out the rest of the articles below — links will be added as posts go live:

Welcome back! Part two in our series intends to give you an overview of the type variable, which you can find in a given dataset. It’s essential to know the different variables’ types to better to engineer them.

Continue reading Hands-on with Feature Engineering Techniques: Variables Types

This article is a part of a series about feature selection techniques. You can check out the rest of the articles as they are/become available:

Welcome back! This post will give you an overview of wrapper methods for feature selection.

In the last post in the series, we explored the filter methods that tend to select features independently and work with (essentially) any machine learning algorithm. Consequently, one of the disadvantages of these methods is that they tend to ignore the effect of the selected feature subset on the performance of the algorithm.

Continue reading Hands-on with Feature Selection Techniques: Wrapper Methods