Articles Fritz has written:

How Do AI-Based Drones Work?

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Drones are unmanned, aerial devices used for a variety of purposes. When first developed, these devices were manually, remotely controlled. Now, however, drones often incorporate artificial intelligence, automating some or all operations.

The incorporation of AI enables drone vendors to use data from sensors attached to the drone to collect and implement visual and environmental data. This data enables autonomous or assisted flight, making operation easier, and increasing accessibility. As a result, drones have become part of the smart mobility offerings that are now commercially available to businesses and consumers.

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Backpropagation Broken Down

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Backpropagation is one of the primary things one learns when one enters the field of deep learning. However, many people have a shadowy idea of backpropagation as it is explained in many beginner-level courses as an intuitive way of following gradients of the loss function through the layers, but seldom referenced mathematically.

Wherever mathematics is involved (especially in deep learning resources and papers) things get a bit too complex and equations are expected to explain themselves at times. Deep learning nowadays is no longer restricted to students at graduate school or research scholars, but is also explored by undergraduates — who tend to skip the mathematical part as it often seems boring.

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Nesting Tab and Stack Navigators in React Native and Expo Apps

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Navigation is an essential part of any mobile app that has multiple screens leading to a variety of interactions for the user. Building a working navigation system often requires us to create a structure that allows for different kinds of navigators working together.

Using react-navigation, you can definitely nest different types of navigators in a React Native app. The term nesting, when it comes to navigators, refers to rendering one navigator inside a screen of another navigator.

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Introduction to RealityKit on iOS— Entities, Gestures, and Ray Casting

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The introduction of iOS 13 brought a major upgrade to Apple’s augmented reality framework. ARKit 3 arrived with a lot of interesting new features — people occlusion, motion tracking, simultaneous front and back camera, and collaborative support. These enhancements to ARKit strongly indicate Apple’s ambition for pushing AR immersion even further.

Up until iOS 12, we had SceneKit, SpriteKit, and Metal as the primary rendering frameworks. Among these, SceneKit, the 3D graphics framework, had been the most logical choice for building ARKit apps.

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Image Effects for Android Using OpenCV: Vertical and Horizontal Stitching

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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.

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Computer Vision at Tesla

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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.

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Handling Location Data in Android

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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.

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Machine Learning Model Optimization

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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.

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