In the highly-competitive modern marketplace of web applications, it’s hard to get to the top and even harder to stay there. This is especially true if you’re just entering the market. Hundreds of web apps are released every day, and their inventors strive to grab the attention of new users and potential customers, trying to outshine the others.
Continue reading Why You Should Optimize Your Web App Testing with NLP
One of my most frequently requested tutorials has been to have augmented reality objects interact with users in the physical world. Many have asked me for a tutorial on having AR objects follow them around.
So in the spirit of those requests, what we’re going to do today is write a simple waypoint system. Here’s basically how it’ll work: upon firing up the app, we’ll begin tracking positions, and as we move around, we’ll add waypoints that any object we add to the scene will follow.
Continue reading Writing a simple Waypoint System in Augmented Reality on iOS
Apple’s “live-streamed” event from Apple Park brought improvements to two of their product lines: the iPhone and the HomePod. The most significant improvements were in the iPhone lineup, the most important of which was 5G capabilities and significantly more advanced cameras on the iPhone 12 Pro line. This article will give you a quick rundown of the releases in this year’s Apple event, so you don’t need to watch the full Keynote.
In Facebook’s Spark AR Studio we can use 2D textures to create animations.
We can either:
Note: While importing a GIF we must ensure that it doesn’t contain any real or realistic images of people whether real or fictional or the effect won’t be accepted for publishing.
So which textures can we import? We can import:
Continue reading 2D Texture Animations in Facebook’s Spark AR Studio
Francois Chollet puts it concisely:
For many deep learning problems, we’re finally getting to the “make it efficient” stage. We’d been stuck in the first two stages for many decades, where speed and efficiency weren’t nearly as important as getting things to work in the first place. So the question of how precise our calculations need to be — and whether we can manage with lower precision — wasn’t often asked.
The food processing industry is benefitting greatly from the latest advancements in artificial intelligence, which is doing everything from helping to sort foods, maintaining top-notch health and safety compliances, developing new products, and bolstering the supply chain. The technology is essentially helping to streamline work processes, making the work of employees easier and making operations more efficient.
Continue reading 4 Applications of Artificial Intelligence in the Food Industry
I’ll be introducing the tools and principles we’ll be using for this tutorial, outlining the process at a high level, and then doing a more in-depth walkthrough. Lastly, I’ll go through the processing code in a bit more detail to explain how everything works. And if you’re up for it, I’ll have a little quiz / FAQ at the end. But if you just want to get things up and running quickly, just follow the steps in the Process section. You can get the whole thing running in just a few minutes!
Continue reading Animated StyleGAN image transitions with RunwayML
Human thoughts are persistent, and this enables us to understand patterns, which in turn gives us the ability to predict the next sequence of actions. Your understanding of this article will be based on the previous words that you’ve read. Recurrent Neural Networks replicate this concept.
RNNs are a type of artificial neural network that are able to recognize and predict sequences of data such as text, genomes, handwriting, spoken word, or numerical time series data. They have loops that allow a consistent flow of information and can work on sequences of arbitrary lengths.
Continue reading An Intro Tutorial for Implementing Long Short-Term Memory Networks (LSTM)
Computer-based processing and identification of human voices is known as speech recognition. It can be used to authenticate users in certain systems, as well as provide instructions to smart devices like the Google Assistant, Siri or Cortana.
Essentially, it works by storing a human voice and training an automatic speech recognition system to recognize vocabulary and speech patterns in that voice. In this article, we’ll look at a couple of papers aimed at solving this problem with machine and deep learning.
Continue reading A 2019 Guide for Automatic Speech Recognition
At every point in time during training, a neural network has a certain loss, or error, calculated using a cost function (also referred to as a loss function). This function indicates how ‘wrong’ the network (parameters) is based on the training or validation data. Optimally, the loss would be as low as possible. Unfortunately, cost functions are nonconvex — they don’t just have one minimum, but many, many local minima.
Continue reading An Empirical Comparison of Optimizers for Machine Learning Models
