Mobile machine learning is still, relatively speaking, in its infancy. As such, new developer tools, frameworks, and environments are regularly in flux.
It can be hard to keep up with the rapid advancements happening at this emerging intersection.
Continue reading “Mobile Machine Learning: Development Tools and Frameworks”
Pneumonia is an inflammatory condition of the lung affecting the alveoli. Typically, symptoms include some combination of productive or dry cough, chest pain, fever, and trouble breathing. According to UNICEF pneumonia remains the leading infectious cause of death among children under five, killing approximately 2,400 children a day.
Early diagnosis of this treatable condition is very important, as it could facilitate earlier treatment and hopefully result in improved clinical outcomes. There are several tests used to confirm diagnoses, including analysis of chest X-ray images.
Continue reading “Detecting Pneumonia in an iOS App with Create ML”
While working with a huge list of data, it’s often convenient to quickly go through the list by means of a scrollbar. As a matter of fact, while reading this article itself, you can see a scrollbar on the right side of the page, which you can use to quickly navigate to the end of the page.
This design pattern is quite common, and Android is no exception, as we can easily find numerous examples of scrollbars, especially when the content is being displayed in a list. Y
ou can see some examples of this below:
Continue reading “Adding fast scroll to RecyclerView in Android”
Imagine we have a set of labeled and unlabeled data, and we want to build a classifier which takes the unlabeled data as input and labels that data as output.
With this kind of situation, we’ll need to build a classification model that will learn from already-labeled data (training data). Later we’ll use that model to predict our unlabeled data (test data).
This type of machine learning is called supervised learning, which we can define as feeding data into a machine learning algorithm.
In doing so, we’re actually showing that groups exist, and which data belong to which groups.
Continue reading “Implementing K-Nearest Neighbors in Your Machine Learning Model”
Have you ever played mobile games like Subway Surfers or Temple Run and wondered how the position of the runner changes?
Have you ever counted the number of steps/distance you’ve walked in a day? Have you ever used a compass in your phone and wondered how it works?
Or ever thought about while talking to a person through your phone, how the phone’s screen goes black when you bring it close to your ear?
Continue reading “Sensors in Android – Everything You Need to Know”
In the final part of our series (we’ve arrived!) we’ll add some polish to our game. To do this, we’ll refine our level by adding a menu system (main and pause menus), which is a necessary part of any game. We will also add background sound to our game.
Before proceeding forward, a quick recap. We started by learning Unreal basics—how to navigate and create a HUD (heads up display).
We also created some enemy AI and winning and losing mechanisms. We even tested the game on an Android device. These are core components of any mobile side-scroller game. In this final part we’ll polish our game so that it can be presentable.
Continue reading “Creating a side-scroller mobile game with Unreal Engine 4: Adding Menus and Sound”
With this article we aimed to create a guide for data scientists and machine learning enthusiasts who are looking for alternatives to Amazon SageMaker.
We explore the top alternatives available in the market, and provide insights into their features, benefits, and limitations. The article aims to equip readers with the knowledge and practical guidance they need to make informed decisions when choosing a machine learning platform.
We’ll also discuss the criteria you should consider when choosing a machine learning platform, including cost, ease of use, features, scalability, and integration with other tools.
Continue reading “6 Best Alternatives to Amazon SageMaker (Reviewed for 2024)”
Deep Learning requires a lot of computations. It typically involves neural network(s) with many nodes, and every node has many connections — which must be updated constantly during the learning. In other words, at each layer of the network, hundreds and thousands of identical artificial neurons perform the same computation.
Therefore, the structure of a neural network fits very well with the kinds of computation that a GPU (Graphic Processing Unit) can efficiently perform — which are designed to compute, in parallel, the same instructions.
Back when I first started using Photoshop, I’d waste hours between classes cropping out pictures of my friends and pasting their faces onto celebrities and teachers. My go-to method was using the Magic Wand tool to select a person, and then creating a crude mask to cut and paste onto another background. This had varying degrees of success and usually resulted in having to manually outline and erase unwanted pixels from my selection.
Continue reading Image Segmentation for Android — Smart Background Replacement with Fritz AI
As mobile application developers, we’re all aware that the app bundle size (APK/IPA) is always a matter of concern. This is especially true when it comes to developing with Xamarin—large bundle sizes is a well-known drawback with Xamarin apps. They can be really heavy! But then the question is, how do you deal with this? Let’s find out!
Continue reading Reducing iOS and Android App Size in Xamarin
