TensorFlow.js is a very powerful library when it comes to using deep learning models directly in the browser. It includes support for a wide range of functions, covering basic machine learning, deep learning, and even model deployment.
Another important feature of Tensorflow.js is the ability to use existing pre-trained models for quickly building exciting and cool applications.
When building an app, sometimes we find ourselves creating data just so we can see our UI start to come to life. Whether we’re adding a new feature, making a POC (proof of concept), or starting from scratch, it’s very easy to just put data together for the sake of it.
For instance, we’ve been building a Team Profiles app to learn SwiftUI. And, so far, we’ve managed to make a class for our Profiles, but even with just three instances, our UI code looks bulky. It takes up more space in our code than the actual UI code does in the ContentView!
When working with standard photo editors, users will generally encounter a common suite of tools — scaling, cropping, color, rotation, blurring, filtering, etc.
But what do users do when they want something a little more powerful and complex? Until recently, the answer has most likely been to transfer photos to powerful desktop editing platforms.
But heavyweight editing tools can be both difficult to learn and cost-prohibitive. And with recent advances in smartphone cameras, more and more high-quality photography is happening on mobile devices. Moving back and forth between a phone’s camera roll and desktop software can quickly become cumbersome, especially when working with multiple images.
Unnecessary background within an image, photograph, or video makes the image lose its focal point. By removing the background, we can draw the attention of the viewer back to the focal point, thereby making the image more informative and easy to view.
In this article, we will learn to create segmentation and the camera texture to create an effect that replaces unnecessary background in Facebook’s Spark AR Studio. We will make use of a segmentation mask texture to separate the foreground and background of the scene. Then, we will implement the camera texture to a material in the foreground to stream a video of the person using the effect back into the scene. You can then transform the environment behind them and replace the background.
Continue reading Replace the Background in Facebook’s Spark AR Studio
Often in machine learning, and specifically with classification problems, we encounter imbalanced datasets. This typically refers to an issue where the classes are not represented equally, which can cause huge problems for some algorithms.
In this article, we’ll explore a technique called resampling, which is used to reduce this effect on our machine learning algorithms.
Continue reading Resampling to Properly Handle Imbalanced Datasets in Machine Learning
For data scientists, checking correlations is an important part of the exploratory data analysis process. This analysis is one of the methods used to decide which features affect the target variable the most, and in turn, get used in predicting this target variable. In other words, it’s a commonly-used method for feature selection in machine learning.
Continue reading Seaborn Heatmaps: 13 Ways to Customize Correlation Matrix Visualizations
In these unprecedented times involving a worldwide pandemic, many changes have taken place in our daily lives. To ensure the safety and well being of all, many protocols and guidelines have been implemented across society, such as maintaining a minimum distance or limiting physical contact with exposed and shared surfaces.
Businesses are getting creative by adapting their shopping experiences to ensure the safety of their customers and employees. Contactless services have become more of a necessity than a fad, and technology has evolved to keep pace with these newfound demands. Many stores have standardized Scan & Go technology, which allows customers to scan item barcodes and pay using just their smartphones, thus limiting human contact and providing a faster, simpler alternative.
Continue reading Safer and Smarter: Contactless shopping with on-device object detection
At WWDC 2020, Apple introduced a bunch of great new updates to SwiftUI to make it even easier for developers to write apps for Apple platforms. In this tutorial, you’ll learn how to use those new features to make your app work on both iOS and macOS. By the end of this tutorial you will have created a fully functional HackerNews reader.
If you’re tired of building and running your iOS apps via Xcode’s ▶️ button, let’s explore an exciting way to build and run your apps without touching the ▶️ button (ever again).
This is a four-step process.
Xcode comes with a number of command line tools. These tools are capable of performing pretty much every thing you can do via Xcode’s UI. While you need a human to point and click certain buttons in Xcode to make it work, these command line tools can help automate the whole process of building and running your project. Powerful, right?
The ability of a machine or program to identify spoken words and transcribe them to readable text is called speech recognition (speech-to-text). In this tutorial, I will be walking you through analyzing speech data and converting them to a useful text for sentiment analysis using Pydub and SpeechRecognition library in Python.
Sentiment analysis is the use of natural language to classify the opinion of people. It helps to classify words (written or spoken) into positive, negative, or neutral depending on the use case. The sentiment analyzed can help identify the pattern of a product; it helps to know what the users are saying and take the necessary steps to mitigate any problems.
Continue reading Sentiment Analysis of Speech Using PyDub and SpeechRecognition in Python
