Author: Fritz

User-generated content has increased significantly in the recent past. Much of this content is text-based, generated mainly via online forums and social media platforms, an will often contain users’ opinions about organizations or hot-button issues.

Businesses exist to provide goods and/or services, which means that communication and relationships with customers are crucial elements of their success. Analyzing customer feedback—either customer reviews or complaints—shared on online or social medium platforms can provide key insights necessary to optimize customer service. In fact, there a lot of statistics that suggest this kind of analysis via user-generated content is a key part of any brand strategy.

Continue reading Using Aspect-Based Sentiment Analysis to Understand User-Generated Content

There’s this farmer. His cows have stopped producing milk so he enlists a group of academics from the local university to help. The team arrives at the farm and the psychologists, mathematicians, physicists, and biologists start collecting pages of data, measuring and observing every move a cow makes.

One by one the academics leave telling the farmer they need time to analyze their data. At last there is one scientist left, a physicist. He walks over to the farmer, “I have a solution,” he says “assume a spherical cow…”

Continue reading Why do neural networks work?

Face mesh provides a 3D mesh that mimics the user’s facial movements. You can add 3D masks using the face mesh which makes your lenses more interesting. In this article, we will create a mask using the material provided in the asset library. Then, we’ll attach objects to the mesh so that they can move with the user’s face movements.

Continue reading Working with Face Mesh in Lens Studio

If you went back in time—2 years ago, let’s say—and asked me to write an algorithm that could take an image of a hand and identify whether it’s making the symbol for a rock, paper, or scissors, I would have said, I doubt it, but give me 6 months.

Continue reading Using TensorFlow.js to Train a “Rock-Paper-Scissors” Model

Everyone knows Siri, and many people use it every day. Why? Because Siri provides a very fast and user-friendly way of interacting with an iOS device.

Convenience is not the only motivation for this type of interaction, though. The combination of speech recognition and speech synthesis feels more personal than using a touch screen. On top of that, the option for verbal communication enables visually impaired people to interact with your app.

Continue reading Speech recognition and speech synthesis on iOS with Swift

In the previous article of this series on developing Flutter applications with TensorFlow Lite, we looked at how we can develop Digit Recognizer with Flutter and TensorFlow Lite, Image Classification with Flutter and TensorFlow Lite, and Object Detection with Flutter and TensorFlow Lite.

Continue reading Turning the Mobile Camera into a Real-Time Object Detector with Flutter and TensorFlow Lite

Neural style transfer allows you to recover the “style” of an image and apply it to content another. This allows developers, with very little effort, to copy the style of a great master and apply it to the picture of their cat (as just one example). Very interesting perspective!

Neural style transfer, or style transfer, has recently become quite popular, especially with the notoriety of applications such as Prisma. It emerges from a context of strong development of neural networks for various applications, and especially for art. And a few months ago, Deep Dream appeared — a program that highlights non-existent patterns in images, creating what could be considered an artistic style in its own right.

Continue reading Style Transfer iOS Application Using Convolutional Neural Networks

The processes surrounding mobile app development have rapidly evolved in recent years. Practices like Mobile DevOps and CI/CD establish the infrastructure needed to speed up app development. However, mobile app testing hasn’t evolved as much to keep up with the speed of other elements of app development.

Continue reading Artificial Intelligence in Mobile App Testing with Bitbar’s AI Testbot

This tutorial uses a genetic algorithm (GA) for optimizing the 8 Queen Puzzle. Starting from an initial state of the puzzle where some queens may be attacking each other, the goal is to evolve such a state using GA to find a state in which no 2 queens are attacking each other.

Optimization is a crucial part of developing any machine learning (ML) application. Despite being simple, GA proves that it’s a powerful technique for solving different types of ML problems. One of the areas that tests this optimization technique is game solving.

Continue reading 8 Queen Puzzle Optimization Using a Genetic Algorithm in Python

Artificial production of human speech is known as speech synthesis. This machine learning-based technique is applicable in text-to-speech, music generation, speech generation, speech-enabled devices, navigation systems, and accessibility for visually-impaired people.

In this article, we’ll look at research and model architectures that have been written and developed to do just that using deep learning.

But before we jump in, there are a couple of specific, traditional strategies for speech synthesis that we need to briefly outline: concatenative and parametric.

Continue reading A 2019 Guide to Speech Synthesis with Deep Learning