In this part of our tutorial series exploring Unity’s AR Foundation framework, we’ll learn how to detect planes and place objects on those planes. We will also learn how to use C# to create scripts and add programmable behavior to our app. If you have not read the introduction to this series, I suggest doing so, as this is a continuation from our overview.
Continue reading Augmented Reality Using Unity’s AR Foundation Framework
This tutorial is the seventh chapter of our implementation of an AirBnB clone in React Native. In the previous chapter, we successfully implemented animated checkmarks for our Login form. In case you need to get caught up, here are links to parts 1–6:
In part 7, we’re going to continue where we left off by implementing a “forgot password?” screen. The forgot password screen is available in most web and app systems in case a user forgets their login credentials. Here, the idea is to create a simple forgot password screen with an email input form. Then we’re going to use Firebase to handle the backend configuration.
Continue reading AirBnB Clone with React Native: “Forgot Password?” with Firebase
Most of the time, when we start working with machine learning, we tend to use models that are readily available online so that we don’t have to spend time and effort creating and training them on our own. But this comes at a cost, as these models don’t usually fit our requirements exactly as expected. Fortunately, Apple has provided Create ML to address those issues, and in this article we’ll learn how to use our own data and categories to train our models.
Continue reading Build iOS-ready machine learning models using Create ML
Phew, okay. After writing part 1 in this series looking deeper into the AI and machine learning landscape, I needed to take a deep breath.
I came across so many awesome companies, organizations, and tools for data labeling, generation, and preparation—it left me impressed but also with a key question in my journey: Now that we have all we need to prepare our datasets, what’s next?
Continue reading AI & Machine Learning Landscape (Part 2): Training platforms and tools
We’re living in an era of transition from inventions of the Renaissance to the technological revolution of automation. Even so, a lot of valuable records and information are still locked inside unstructured documents. Transforming these documents into digital format to perform various operations requires complex AI techniques on top of simple optical character recognition.
Today, a number of industries including medicine, finance, law, and real-estate rely on human-intensive processes to convert forms and other documents into digital formats. Be it patients history locked in medical record files of hospitals, mortgage applications, or tax forms, these transcriptions all requires effort and human intervention on top of simple OCR techniques — that used to detect text without keeping the composition of document intact — to convert them into valuable digital assets.
Continue reading Amazon Textract — Going beyond optical character recognition (OCR)
If there’s one thing that developers like more than a bug-free code, it’s a properly implemented dark theme! Be it on their IDEs or in the apps they use, dark theme is an integral part of the entire developer culture.
And while it not only looks good, it has several other benefits like:
With the introduction of Android 10, Google brought the much-awaited Dark Mode to the native Android framework and system apps.
Continue reading Adding Dark Mode Support to Your Android Apps
Depth sensing is a classic problem with a long history of prior work. It’s at the heart of many tasks, from 3D reconstruction to localization and tracking. Its applications span otherwise disparate research and product areas, including indoor mapping and architecture, autonomous cars, and human body and face tracking.
Continue reading ActiveStereoNet: The first deep learning solution for active stereo systems
The world is currently generating 2.5 quintillions of data each day, according to Forbes. The power of deriving meaning from these loads of data relies on the use of specialized machine learning algorithms that have been developed over time. We can teach machines to learn—or they can learn on their own.
Continue reading A guide to the top machine learning algorithms
A Convolutional Neural Network (CNN) is a multilayered neural network with a special architecture to detect complex features in data. CNNs have been used in image recognition, powering vision in robots, and for self-driving vehicles.
In this article, we’re going to build a CNN capable of classifying images. An image classifier CNN can be used in myriad ways, to classify cats and dogs, for example, or to detect if pictures of the brain contain a tumor. This post will be at an introductory-level, and no domain expertise is required. However, we assume that the reader has a basic understanding of Artificial Neural Networks (ANN).
Continue reading Convolutional Neural Networks: An Intro Tutorial
In the last couple of years, machine learning has opened up new horizons in a wide range of industries, with advanced use cases emerging: Facebook’s facial recognition, Netflix’s recommended movies, PrismaAI’s image style transfer, Siri’s voice recognition, Google Allo’s natural language processing, and the list goes on.
Continue reading 25 Open-Source Machine Learning Repos to Inspire Your Next Project
