Author: Fritz

Over the past 3 years, our incredible community of contributing writers, developers, and creators have helped us explore the foundations of machine learning, mobile development, and where these two areas of tech intersect.

To date, we’ve published work from more than 150 individual contributors from over a dozen countries around the world — those voices have helped build an incredible library of foundational blog posts and articles that offer both beginners and seasoned developers educational resources, industry insights, research reviews, and much more.

Continue reading “Heartbeat Contributor Program: Announcing new content topics”

Recently, I wrote two articles about training object detection Core ML models for iOS devices using TensorFlow and Turi Create frameworks.

To train those models, I used a tool I’d built called MakeML. It allows you to easily create a dataset, label it, and start training. There’s no need to write code with MakeML, so every iOS developer can train an object detection machine learning model in a couple of hours. On paper, at least…

Continue reading “MakeML’s Automated Video Annotation Tool for Object Detection on iOS”

Generally, in machine learning problems, we often encounter too many variables and features in a given dataset on which the machine learning algorithm is trained.

The higher the number of features in a dataset, the harder it gets to visualize and to interpret the results of model training and the model’s performance. Moreover, when dealing with such a massive dataset in terms of features, the computational costs should be considered.

Continue reading “Demystifying Principal Component Analysis: Handling the Curse of Dimensionality”

In part one of our series on SwiftUI, we began by making a simple profile page using SwiftUI. We got as far as:

Displaying our picture and name using Views Setting them in a VStack Apply Modifiers to have our UI follow basic design style guidelines

In this tutorial, we’ll take it a step further. We’ll learn how we can continue to modify Views, including using other Views as modification parameters. We’ll also explore how we can create our own custom Views, which will help keep our UI code clean, readable, and reusable!

Continue reading “SwiftUI: Creating Custom Views and Manipulating Others”

Running machine learning models on your Jupyter Notebook is fine, but have you ever thought about how these models can run on your mobile device, which has limited space and processing power?

If we use models directly in our mobile application, it will increase the size of the mobile app too much — so how do we manage this on a mobile device? I am going to go through the answers to these questions in this article.

Continue reading “Modeling a Language Translation System using LSTM for Mobile Devices or Web”

TensorFlow only recently concluded its yearly Dev Summit via livestream (due to the COVID-19 global pandemic) and there were a lot of exciting announcements, most focused on propelling machine learning to even greater heights.

From a robust new release of the core TensorFlow platform (TF2.2) to new Google Cloud AI Platform Pipelines for making the use of TensorFlow in production even easier, and beyond.

Continue reading “TensorFlow Lite Model Maker: Build an Image Classifier for Android”

As we already know, machine learning is a way of automating complex problem-solving. But can machine learning itself be automated? That’s what we’ll explore in this article.

By its end, we’ll have answered that question and shown practical ways it can be accomplished.

Continue reading “Automated Machine Learning in Python”