Author: Fritz

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

Push notifications—being one of the most integral parts of a mobile application—should be one of the first things you configure while building your mobile app. This blog will help you get you familiar with the fundamentals of setting up push notifications in your Xamarin.Android project using Firebase.

Firebase Cloud Messaging (FCM) is a cross-platform service that handles the sending, routing and queueing of messages between server applications and mobile apps.

Continue reading Firebase Cloud Messaging for Remote Push Notifications on Android with Xamarin

Custom Vision is a platform that allows you to build your own customized image classifiers. If you want to develop a mobile app where you need to classify images, Custom Vision (by Microsoft) offers you one of the fastest ways to go about it.

With Custom Vision, you simply upload your labeled images, train the model on the platform, and then export a Core ML model (for iOS) or TensorFlow model for Android (and even ONNX for Windows ML and DockerFile for AzureML). Yes, you heard that correctly. And it is free for 2 projects, and up to 5000 training images per project.

Continue reading Build Your Own Customized Image Classification Mobile App in 10 Minutes

Creating accurate machine learning models capable of identifying multiple objects in a single image remains a core challenge in computer vision, but now with the advancement of deep learning and computer vision models for mobile, you can easily detect target objects from an image on Android.

In this article, we’ll do just that with the help of Firebase ML Kit’s Object Detection API.

Continue reading Object Detection in Android Using Firebase ML Kit

In the first part of this series, I introduced deep learning in JavaScript—we explored why you should consider using Javascript for deep learning, and then went on to create a neural network to predict areas acutely affected by forest fires.

Continue reading Deep Learning in JavaScript (Part 2)

Supporting deep learning inference on mobile and edge devices has gained popularity more than ever and we have a greater number of options to choose from when carrying out AI-related development tasks on our little companions than we could have guessed.

Not only is implementing machine learning models—the standard for tasks such as computer vision—faster and easier on mobile devices these days, but the renewed competition between the developers of frameworks supporting them also seems to have ensured that the process itself reaches new heights in terms of performance, flexibility and adaptability.

Continue reading Alibaba’s Mobile Neural Network: A deep learning framework for mobile and embedded devices

Thanks to TensorFlow Lite (TFLite), we can build deep learning models that work on mobile devices. In fact, models generated by TFLite are optimized specifically for mobile and edge deployment for that purpose. After a deep learning model is created in TensorFlow, developers can use the TensorFlow Lite converter to convert that model to a format that runs in mobile devices.

Continue reading Image Recognition for Android with a Custom TensorFlow Lite Model

Sentiment analysis is one of the most popular natural language processing (NLP) applications in the business world. Also known as opinion-mining, it’s a subfield of NLP that analyzes texts and attempts to classify them as positive or negative.

In supervised learning, this would be called a classification problem, wherein the texts have already been labeled and we use these labels to train machine learning models in order to generalize and classify unseen datasets successfully.

Continue reading Evaluating Unsupervised Sentiment Analysis Tools Using Labeled Data

In this article, we’ll see how we can use the Luminoth library to detect objects in pictures or videos. Luminoth is an open source computer vision library built in Python and based on TensorFlow and Sonnet. This library was developed by Tryolabs. You can learn more about Luminoth and some of their other projects here:

Continue reading “Object Detection with Luminoth”