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So you came here—let me guess—it’s either you’re in a data science competition and you read somewhere about how winners of most competitions win with ensembles, or you’re just a curious data scientist who wants to learn about ensembles.

Either way, understanding how ensembles work is a very important knowledge and as data scientists and machine learning engineers, you should be able to employ the skills behind them.

Research has shown that a majority of the time, ensembles will outperform a single model, and it’s the recommended technique for maximizing accuracy or reducing errors in a machine learning model.

Continue reading “Ensemble Learning Techniques Demystified”

In this tutorial, we’ll cover how to implement the neural-style algorithm that’s based on this paper.

What is neural style transfer?

Neural style transfer is a technique used to generate images in the style of another image. The neural-style algorithm takes a content-image as input, a style image, and returns the content image as if it were painted using the artistic style of the style image.

Continue reading “Neural Style Transfer with PyTorch”

LUT or, Look Up Table, is a photo or image filter that enhances and changes the color tone and grading of your image. It essentially can convert colors and details in a source file to a new destination state.

Using a custom lookup table or “LUT” allows us to have free range on creating our own custom color corrections through external third party programs and bring them directly into Lens Studio.

Continue reading “Lens Studio Basics — LUTs”

Machine learning (ML) began its ascent into the medical industry when it acquired the ability to detect visual patterns between images—a skill doctors and technicians take years to master.

Specifically, ML models for computer vision tasks in the medical field train on datasets of separated images to learn to recognize their similarities and differences.

Continue reading “Detecting Skin Cancer on iOS with Xcode and Create ML”

If you got past the jargon of the title, you probably have at least a passing interest in computer vision. However, fear not! This is going to be a fairly gentle walk-through of some of my projects at the intersection of Machine Learning and Augmented Reality.

They all share a common denominator: feature point extraction. InstaSaber, Say BARK!, and the puppet videos you see below are a few examples.

Continue reading “Real-Time 2D/3D Feature Point Extraction from a Mobile Camera”

Throughout the Android chat app development series, we’ve built an app that allows users to register, send/receive text messages, open conversations, and receive notifications for incoming messages.

One additional essential feature for chat apps is the ability to update the active, live conversation. Once a new message arrives for the currently active conversation, it should be updated to reflect the new changes (either sent/received messages).

Continue reading “Live Conversation Updates for New Messages in an Android Chat Application”

Introduction

In recent years, AI and (more specifically) machine learning have become so popular that the hype surrounding them has become contagious.

It’s becoming apparent that the widespread use of deep learning doesn’t stop at just computer scientists and programmers but has made its way into every domain, ranging from physics, biology, and chemistry to healthcare, transportation, and finance.

Continue reading “How next-gen AI accelerators will transform mobile machine learning”

In Spark AR Studio, you use the Simulator to perceive how an effect will appear on a device while you are creating it.

The Simulator is positioned on the top right side of the Spark AR Studio interface. You will see that the Simulator previews an effect on an iPhone 8 by default.

Continue reading “Understanding the Simulator in Spark AR Studio”