Author: Fritz

Hello readers! In this article I’ll be discussing another cool trick we can do with deep learning. Specifically, we’ll see how to change the background in an image or in a video, just like they do in news rooms and in movies.

Continue reading “Changing Image Backgrounds Using Image Segmentation & Deep Learning”

Machine learning has changed the way users interact with mobile applications. It offers brand new experience to users, making apps capable of leveraging various features such as providing accurate, location-based recommendations; detecting and manipulating text from images; instantaneously detecting micro diseases, and much more!

Continue reading “Card Scanner on Android Using Firebase’s ML Kit and CameraX”

Let say, for some reason, you process a huge load of images that contain some sort of logo (or even products). And you want to know which images are related to a specific companies. This is very common problem in AdTech and/or Data Mining companies.

Let’s imagine you have a user generated content in your application, so you want to know what’s inside the images, and if you have ads running (Bingo!), you can target a user based on the images they liked, commented on, published etc.

Continue reading “Logo Recognition iOS Application Using Machine Learning and Flask API”

Lobe, owned by Microsoft, is a free, no-code tool to train machine learning models without technical skills. Only image classification is supported as of this writing, and an object detection model training is coming soon according to Lobe’s homepage. You can download it here by entering your basic info. It’s 608 mb.

Continue reading “Train a Face-Mask Detection Model in Under 5 Minutes using Lobe.ai”

Innovation starts with solving personal problems. Every chess player writes down their moves on a chess scoresheet during a tournament game to analyze soon after. The annoying part is you have to record your moves once during the game, and then again into a computer to analyze the moves you played.

On top of this, it’s super easy to keep highly disorganized computer files (keeping track of moves in our minds, unfortunately, doesn’t translate well to keeping track of papers in real life). Instead, we store loose scoresheets in the musty depths of a backpack — crumpled up to oblivion.

Continue reading “Scannable Chess Scoresheets with a Convolutional Neural Network and OpenCV”

Over the last few years, generative models have been on the rise thanks to breakthrough innovations, especially in the domain of deep learning. We are now on the verge of solving complex tasks that seemed impossible 10 years ago.

There are countless applications for these techniques, so in this mini-series, we’ll focus on what they could bring to our handheld companions. In the first part let’s take a look at a few applications in image generation and transformation.

Continue reading “Using Generative Deep Learning Models On-Device”

In this article, we’ll use unowned references to break retain cycles in the object memory graph, learn how to handle memory in closures, and explore closure capture lists.

Unlike weak references, unowned references are used with non-optional types. Hence, it can’t go away if the reference type it’s pointing to goes away; instead it traps.

Continue reading “Advanced Memory Management in Swift”