Ever want to manage photo clutter on your phone? If you’re like me, desperately snapping several photos trying to get the best angles, pictures and videos slowly start accumulating on your phone. Eventually it runs out of memory, and you’re stuck deciding what to delete.
Continue reading “Community Spotlight: Zyl, a smart photo gallery for your phone”
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 guidelinesIn 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.
Many powerful and well-equipped image editing tools are available that allow you to remove backgrounds, much like a green screen effect. These tools are packed with powerful features that enable us to make powerful and transformative edits to images—background removal being a key functionality.
But what if we could create our own background removal tool? Interestingly, we can use AI to do just this, automatically segmenting out a person’s body from an image and then cutting out the background.
Continue reading “Getting Started with Image Segmentation Using TensorFlow.js”
Small Planet is a New York based development shop that builds apps and digital experiences for companies like FanDuel and Disney. Recently, they’ve started a Machine Learning Lab to do take their projects to the next level with AI, Core ML, and TensorFlow.
We came across Small Planet after finding their awesome Pinball Wizard project where their team trained a neural network to play pinball. Not satisfied with simply playing a digital pinball game, the Small Planet team hooked an iPhone 6 up to an Onion Omega2 IoT computer so that it could manipulate the flippers of a real machine.
Machine learning is only possible when there’s enough of data available. Data collection is one of the most vital components of the machine learning lifecycle, but it’s also the one that’s often overlooked.
Web scraping—programmatically collecting data from web pages—is often the primary technique used to collect information from the Internet. While web scraping can be very straightforward in some instances, it can be very tricky in other instances. Especially when the data we’re interested in is hidden behind a button that requires a mouse click.
Continue reading “Building an Advanced Web Scraper for Kaggle Leaderboards Using RSelenium”
While looking to try my hand at using Apple’s Core ML framework for on-device machine learning, I stumbled upon a lot of tutorials. After having tried a few, I decided to come up with my very own 😛
Turns out, it was a good exercise! In this tutorial, I’ll walk you through the development of an app that uses a pre-trained Core ML model to detect a person’s age from an input image. So here we go.
Continue reading “Using Core ML and Vision in iOS for Age Detection”
