Very rarely do we find mobile apps that have no connection to a backend of any sort. There are many services and platforms like Azure or AWS, but one Google product stands out as well: Firebase. It’s easy to use, scalable and—most importantly—free. In this tutorial, we’ll explore how to connect our Flutter mobile app to Firebase.
Continue reading Using Firebase’s Cloud Firestore in Flutter
Coil is a new image loading library in Kotlin for downloading images in Android, and it’s list of strengths are impressive:
Before Coil, we often used libraries like Glide and Picasso to download images in Android. But with the improvements that we’ll cover in this article, it’s clear that Coil is the go-to choice for Android devs working with Kotlin.
Continue reading Using Coil to Download Images on Android in Kotlin
Many packages in Python also have an interface in R. Keras by RStudio is the R implementation of the Keras Python package. Most of the functions are the same as in Python. The only difference is mostly in language syntax such as variable declaration.
We live in the world with a vast array of mobile devices, primarily dominated by two platforms: iOS and Android. It’s a two-horse race, and I’m sure we can all agree on that. Building a mobile application for both platforms is not an easy task, though.
For iOS, you write code using Objective-C or Swift, and for Android, you will find yourself using Java or Kotlin. Apart from different programming languages used to create an app that can run on each of the two platforms, the toolchains are entirely different.
Continue reading “Getting Started with React Native and Expo using Hooks 2024”
Hello everyone! My name is Yuma Soerianto. Today, I’ll show you how to make a simple Augmented Reality game using Apple’s ARKit framework and Xcode.
This is a finished product of what we’re going to build. It’s a simple game where you shoot and knock down crates.
Continue reading Using ARKit on iOS to Build an Augmented Reality Shooter Game
In the last post, you created a 2-layer neural network from scratch and now have a better understanding of how neural networks work.
In this second part, you’ll use your network to make predictions, and also compare its performance to two standard libraries (scikit-learn and Keras).
Continue reading Building a Neural Network From Scratch Using Python (Part 2)
This tutorial builds a login system for an Android app. The app allows the users to register using a username and a password. A local server implemented in Python using Flask accepts the user data and inserts them into a MySQL database.
For securing the password, it’s encrypted using the MD5 hashing algorithm. After successful registration, the user will be able to log into the system by entering a server-verified username and password.
Do you remember climbing trees in your childhood? According to researchers, at the University of North Florida, climbing a tree can dramatically improve cognitive skills, including memory. Climbing trees can help children become more flexible in body and mind, while also developing strong spatial reasoning skills.
Continue reading Implementing Regression Using a Decision Tree and Scikit-Learn
This post is a part of a series about feature engineering techniques for machine learning with Python.
You can check out the rest of the articles:
Welcome to the last of this series on feature engineering! In today’s article, we’ll explore some advanced feature engineering techniques across different tasks. Specifically, we’ll look at advanced categorical encoding, advanced outlier detection, automated feature engineering and more.
Continue reading Hands-on with Feature Engineering Techniques: Advanced Methods
At I/O 2018, Google announced the release of Firebase’s ML Kit, a developer-friendly software package that allows mobile engineers to quickly integrate Machine Learning features in their applications with just a few lines of code. With ML Kit, we’re able to do amazing things like face detection, text recognition, and landmark recognition, all without needing to have deep knowledge about neural networks or model optimization.
