Whether it’s handling and preparing datasets for model training, pruning model weights, tuning parameters, or any number of other approaches and techniques, optimizing machine learning models is a labor of love.
It’s important to note that there’s no one-size-fits-all approach: different use cases require different techniques, and various stages of the model building lifecycle determine possible and preferred optimization strategies.
Scientists have been experimenting for some years now on ways to make computers recognize music notations. According to a paper by Jorge Calvo-Zaragoza et al, research has been done in this area of study for the past 50 years and these involved the use of different techniques most of which were based on cutting edge technology present at those times.
Continue reading Play Sheet Music with Python, OpenCV, and an Optical Music Recognition Model
Hyperparameters have inevitably been used by anyone who has practiced machine learning, either in industry, academia, or even self-study. It can be an intimidating term if you’re just starting your dive into machine learning.
And once you’ve figured out how to pronounce the term, the key to understanding hyperparameters is knowing how they differ from parameters.
In my time as an Android developer, I’ve run into issues with designing app icons for different Android devices and OS versions. Luckily, however, I found a workable solution: to create adaptive icons with Android Studio that work across devices and versions of Android.
Creating an adaptive Icon for Android using Android Studio is relatively easy—but before we jump into an implementation, let’s first take a closer look at what an adaptive icon actually is.
Continue reading Implementing Adaptive Icons in Android Using Android Studio
It’s safe to say that a majority of developers work by consuming REST APIs in their day-to-day tasks. And this is not at all surprising as REST is amazing architecture.
But since the development landscape and the technologies within that landscape are constantly changing, developers need to keep themselves up-to-date with the latest and greatest, especially when it comes to optimizing development time, cost, speed, and more. And to achieve this, GraphQL enters in the picture.
In case you were wondering, no — Fastlane doesn’t have an enterprise offering. It’s always been (and I hope will continue to be) an open-source product.
Fastlane is a collection of tools that help you automate all aspects of mobile development, such as creating new app IDs in the developer portal and managing provisioning profiles; building, testing and packaging apps; and distributing apps to the App Store.
Continue reading Working with Fastlane on iOS in an Enterprise Environment
This tutorial is a quick guide on how to integrate Facebook login with Firebase while developing with React Native. By using Firebase and Facebook login, you’ll be able to understand how to use and integrate Firebase properly, as well as implement Facebook authentication along with login. This process will also help developers learn a lot about Firebase, which can be used as lightweight and easy-to-configure backend.
Continue reading Implementing Facebook login on Android with React Native and Firebase
Mercari is a popular shopping app in Japan that has been using AutoML Vision (Google’s AutoML solution) for classifying images. According to Mercari, they’ve been “developing their own ML model that suggests a brand name from 12 major brands in the photo uploading user interface.”
ARKit allow developers to layer virtual objects onto real world environment. In this post, we’ll explore how we can make our real hands interact with virtual objects using ARKit and Core ML, Apple’s machine learning framework.
Augmented reality is a lot of fun. ARKit from Apple is a really powerful tool that uses computer vision to analyze your environment and detect features from it. The power lies in the fact that you can use the detected features from the video to anchor virtual objects in the world and give the illusion they are real.
In this piece, we’ll look at some of the AI and machine learning conferences that took place in 2019 and highlight some of the best speakers and presentations. These events form a fundamental part of the machine learning and AI communities because they bring people together to learn from each other as well as forge meaningful collaborations.
