OpenCV is a powerful tool for doing intensive computer vision operations in real-time, even on resource-limited mobile devices. Throughout a couple of tutorials, we’re going to create an Android app that applies various image effects. Some of these effects might use just 1 image, while others might use more than 1 image.
Continue reading Image Effects for Android Using OpenCV: Vertical and Horizontal Stitching
Tesla’s Autopilot is certainly the most advanced in the world. If Comma.ai is the Androïd, Tesla is definitely the Apple of self-driving cars. Recently, Tesla has released “Tesla Vision”, their new system equipped only with cameras… making it one of the only companies in the world not to use RADARs!
This is not the only place where Tesla is going against the crowd, their entire strategy is based on fleet and selling products, while most of its competitors sell autonomous delivery and transportation services. You can learn more about it on my article on Tesla vs Waymo here.
In this blog post, you’ll be learning about how to implement and leverage location data in Android.
Leveraging location data from user devices, you can find nearby shops, cafes, cinemas, hospitals, your time zone, provide location-based suggestions to users, share locations with other users or 3rd parties, reach your destination, and more!
Today, most major applications like Google Maps, Facebook, Uber, Swiggy, etc. are using this feature to provide some of the best services to the users as well.
To help sift through some of the incredible projects, research, demos, and more in 2019, here’s a look at 17 of the most popular and talked-about projects in machine learning, curated from the r/MachineLearning subreddit. I hope you find something inspiring, educational, or both in this list.
Continue reading Best of Machine Learning in 2019: Reddit Edition
Whether it’s handling and preparing datasets for model training, pruning model weights, tuning parameters, or any number of other approaches and techniques, optimizing machine learning models is a labor of love.
It’s important to note that there’s no one-size-fits-all approach: different use cases require different techniques, and various stages of the model building lifecycle determine possible and preferred optimization strategies.
Scientists have been experimenting for some years now on ways to make computers recognize music notations. According to a paper by Jorge Calvo-Zaragoza et al, research has been done in this area of study for the past 50 years and these involved the use of different techniques most of which were based on cutting edge technology present at those times.
Continue reading Play Sheet Music with Python, OpenCV, and an Optical Music Recognition Model
Hyperparameters have inevitably been used by anyone who has practiced machine learning, either in industry, academia, or even self-study. It can be an intimidating term if you’re just starting your dive into machine learning.
And once you’ve figured out how to pronounce the term, the key to understanding hyperparameters is knowing how they differ from parameters.
In my time as an Android developer, I’ve run into issues with designing app icons for different Android devices and OS versions. Luckily, however, I found a workable solution: to create adaptive icons with Android Studio that work across devices and versions of Android.
Creating an adaptive Icon for Android using Android Studio is relatively easy—but before we jump into an implementation, let’s first take a closer look at what an adaptive icon actually is.
Continue reading Implementing Adaptive Icons in Android Using Android Studio
It’s safe to say that a majority of developers work by consuming REST APIs in their day-to-day tasks. And this is not at all surprising as REST is amazing architecture.
But since the development landscape and the technologies within that landscape are constantly changing, developers need to keep themselves up-to-date with the latest and greatest, especially when it comes to optimizing development time, cost, speed, and more. And to achieve this, GraphQL enters in the picture.
In case you were wondering, no — Fastlane doesn’t have an enterprise offering. It’s always been (and I hope will continue to be) an open-source product.
Fastlane is a collection of tools that help you automate all aspects of mobile development, such as creating new app IDs in the developer portal and managing provisioning profiles; building, testing and packaging apps; and distributing apps to the App Store.
Continue reading Working with Fastlane on iOS in an Enterprise Environment
