Every data science task needs data. Specifically, data that’s clean and understandable by the system it’s being fed into. When it comes to images, a computer needs to see what human eyes see.
For example, humans have the ability to identify and classify objects. Similarly, we can use computer vision to interpret visual data that it receives. That’s where image annotation comes into the picture.
Continue reading Data Annotation Fundamentals (Part 1) — Image Annotation
NumPy supports different ways of generating arrays, and this tutorial is going to explore one way of do so, using the np.linspace() function. It returns evenly-spaced numbers and can generate arrays of any dimensionality.
The following sections are covered in the tutorial:
linspace() is a function that is not only available in NumPy but also available in other tools such as MATLAB. By searching about it, you can find the regular answer which defines linspace() as a function to generate evenly spaced numbers.
Computer vision, at its core, is about understanding images. The field has seen rapid growth over the last few years, especially due to deep learning and the ability to detect obstacles, segment images, or extract relevant context from a given scene.
Using computer vision, we can build autonomous cars, smart buildings, fashion recommender systems, augmented reality tools…the possibilities are endless.
Continue reading Computer Vision and Deep Learning: From Image to Video Analysis
With augmented reality rocking every corner of the App Store and touching every category from gaming to healthcare, now is a better time than ever to jump in and start building your own AR experiences. In this tutorial, you’ll learn to set up and use Reality Composer to bring Mars into your home using the power of augmented reality. Later, you can apply this knowledge to build more advanced AR experiences.
Continue reading Apple’s Reality Composer: Bring Mars into Your Living Room with AR
The process of uploading iOS binary files to the App Store is time-consuming and (let’s face it) boring. And Apple hasn’t provided better options for this process. As of now, we’ve used various tools to upload .ipa files to App Store Connect. The most common method for solo iOS developers is to upload directly from Xcode or use the Application Loader tool, which is integrated in Xcode.
Continue reading Full Stack iOS Continuous Delivery with xcodebuild and ExportOptions Plist
Sales are the lifeblood of a business. Sales forecasting is, therefore, a crucial part of any business’s financial planning.
In this tutorial, we look at one specific sales forecasting example: public bus ticket sales. Accurately predicting the demand for bus ticket sales can enable commuter companies to make informed decisions regarding their operations. The companies are able to gauge customer demand for certain rides, to manage resources and vehicles more efficiently, to offer promotions and sell other services more effectively, such as micro-insurance, or even improve customer service by being able to efficiently communicate alerts and other useful information.
Continue reading Using Machine Learning to Predict Bus Ticket Sales
In this post, we’re going to investigate the field of image super-resolution and its applications in real world. We’ll discuss a brilliant state-of-the-art model involving generative adversarial networks (GANs) for this task and try to understand the underlying logic behind the approach.
So let’s jump right in!
Continue reading Image Super-Resolution using Generative Adversarial Networks
The uses of machine learning and computer vision features in mobile applications are increasing quickly; so much so that users—with some kinds of applications—expect mobile experiences to have embedded artificial intelligence.
Training and deploying ML models to the phone can take a considerable amount of time; you need to have in-depth knowledge of neural networks or model optimization to create a performant model—or hire ML engineers to do these task.
Continue reading Scan Barcodes on Android Using the Power of ML Kit and Fotoapparat
Machine learning continues to be an increasingly integral component of our lives, whether we’re applying the techniques to research or business problems. Machine learning models ought to be able to give accurate predictions in order to create real value for a given organization.
While training a model is a key step, how the model generalizes on unseen data is an equally important aspect that should be considered in every machine learning pipeline. We need to know whether it actually works and, consequently, if we can trust its predictions. Could the model be merely memorizing the data it is fed with, and therefore unable to make good predictions on future samples, or samples that it hasn’t seen before?
Continue reading Introduction to Machine Learning Model Evaluation
Face masks have become a necessity in the current world due to the ongoing COVID-19 pandemic. Many countries and health organizations have advised people to wear masks in public to contain the spread of the virus. Many shops, pubs, mega-stores, and more businesses have strict mask-wearing rules to for customers and employees upon entrance entering.
Businesses and local governments enforcing these strict rules need some kind of mechanism to track people who are not wearing masks. Tracking every single person just with the human eye is more or less impossible.
Continue reading How to Detect Face Masks in Images with TensorFlow.js
