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.
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
In machine learning and statistics, we often have to deal with structural data, which is generally represented as a table of rows and columns, or a matrix. A lot of problems in machine learning can be solved using matrix algebra and vector calculus. In this blog, I’m going to discuss a few problems that can be solved using matrix decomposition techniques. I’m also going to talk about which particular decomposition techniques have been shown to work better for a number of ML problems. This blog post is my effort to summarize matrix decompositions, as taught by Rachel Thomas and Xuemei Chen in the Computational Linear Algebra course at the University of San Francisco. This whole course is available for free as a part of fast.ai online courses. Here is the link to the introductory post by Rachel Thomas about the course.
Continue reading Applications of Matrix Decompositions for Machine Learning
Over the years, many activation functions have been introduced by various machine learning researchers. And with so many different activation functions to choose from, aspiring machine learning practitioners might not be able to see the forest for the trees. Although this range of options allows practitioners to train more accurate networks, it also makes it harder to know which one to use.
Continue reading Benchmarking deep learning activation functions on MNIST
In our previous posts, we presented simple and effective guides for integrating a Dialogflow bot on a website and on Android. In this article, we’ll be sharing steps that show how to build an iOS chatbot with Dialogflow. All you need to build a sample chatbot using Dialogflow and Kommunicate for an iOS app.
Continue reading Building an iOS Chatbot with Dialogflow and Kommunicate
