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
Machine learning systems built for production are required to efficiently train, deploy, and update your machine learning models. Various factors have to be considered while deciding on the architecture of each system. Parts of this blog post are based on the Coursera and GCP (Google Cloud Platform) course on building production machine learning systems. Below, I’ll list some of the concerns in building a scalable machine learning system:
Continue reading Building Production Machine Learning Systems
This tutorial is the 9th part in the series, ML Kit for Mobile Developers. If you’re not quite caught up, you can start here:
Earlier this month at Google I/O, the team behind Firebase ML Kit announced the addition of 2 new APIs into their arsenal: object detection and an on-device translation API.
Continue reading Building a real-time object detection app on Android using Firebase ML Kit
In this tutorial, we’ll learn the basics of creating an intro slider using React Native. To do this, we’ll use a React Native module known as react-native-app-walkthrough, and the result will be a simple and configurable app introduction slider.
The detailed explanation of each step for this tutorial is provided below:
First, we need to simply install the react-native-app-walkthrough package. We can do this using NPM (Node Package Manager) or yarn, and for this, we’ll using NPM. Open your NPM console or command prompt within your project directory and enter the following code snippet:
Continue reading Building an App Introduction Slider in React Native
All the algorithms in machine learning rely on minimizing or maximizing a function, which we call “objective function”. The group of functions that are minimized are called “loss functions”. A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome. A most commonly used method of finding the minimum point of function is “gradient descent”. Think of loss function like undulating mountain and gradient descent is like sliding down the mountain to reach the bottommost point.
Continue reading 5 Regression Loss Functions All Machine Learners Should Know
