Author: Fritz

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.

Continue reading Building an Android Login System

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.

Continue reading Creating an Android app with Snapchat-style filters in 7 steps using Firebase’s ML Kit

Quick answer:

In a nutshell and much more simple words that may not be as comprehensive, AI is the study of automating intelligent decisions.

In order to train and test the methodologies scientists come up with. They need some sort of a playground to experiment in. A playground is an environment that is inspired by reality and agents are the decision-makers.

Continue reading What is AI: Everything You Need to Know

In this post we’re going to embark on a journey to explore and dive deep into the world of optimizers for machine learning models. We will also try and understand the foundational mathematics behind these functions and discuss their use cases, merits, and demerits.

So, what are we waiting for? Let’s get started!

Continue reading Exploring Optimizers in Machine Learning

In this tutorial we’ll cover XGBoost, a machine learning algorithm that has dominated the applied machine learning space recently.

XGBoost is an open source library that provides gradient boosting for Python, Java and C++, R and Julia. In this tutorial, our focus will be on Python. Gradient Boosting is a machine learning technique for classification and regression problems that produces a prediction from an ensemble of weak decision trees.

Continue reading Boosting your Machine Learning Models Using XGBoost