This post is a part of a series about feature engineering techniques for machine learning with Python.
You can check out the rest of the articles:
Welcome back! In this post of our series on feature engineering, we’re going to focus on another common issue in most datasets—outliers. Here, we’ll examine what an outlier is and the different methods to handle them, alongside some code snippets. Let’s get started.
Continue reading Hands-on with Feature Engineering Techniques: Handling Outliers
Augmented reality is a term we’ve all heard of, but few developers are able to create immersive AR experiences — it often requires 3D design skills that are often missed in software development courses.
Thankfully, Apple’s Reality Composer makes it easy to build quick, immersive experiences without having experience with CAD and 3D modeling. While beginners can start with this tutorial, I strongly recommend checking out my previous tutorial for a more basic look at Reality Composer.
Continue reading Apple’s Reality Composer: Put the Moon in Orbit with Augmented Reality
Accessing a device’s calendar in a React Native app can be a bit tricky. Using open source modules like react-native-add-calendar-event is helpful when you’re looking to show a calendar inside a modal or manipulate calendar events (adding, editing, etc).
Continue reading How to add a custom calendar event in a native iOS app using React Native
It’s Friday. I’m checking my phone for the 30th time in the last hour. It’s 1:02 P.M. Something’s wrong. The UPS courier is late. They said it would arrive by 1 P.M — why is it not here? I was preparing one of my fresh daily emails, but I couldn’t focus. And suddenly… DING DONG! 🔔 He’s here! I suddenly felt super excited. It felt like Christmas. This morning, I received my OAK-D Camera…
Continue reading Edge AI: The Ultimate Guide to Vision Processing Units (VPUs)
The main aim of this article is to introduce you to language models, starting with neural machine translation (NMT) and working towards generative language models.
For the purposes of this tutorial, even with limited prior knowledge of NLP or recurrent neural networks (RNNs), you should be able to follow along and catch up with these state-of-the-art language modeling techniques.
WWDC20 has dropped a ton of great updates for developers and users. Noticeable throughout the Keynote and Platforms State of the Union addresses were mentions of Core ML models powering some incredible first-party features, such as the sleep training capabilities. ML continues to play a key role in Apple’s vision for the future.
Continue reading Evolving Your iOS App’s Intelligence with Core ML Model Deployment
Apple’s WWDC 2020 (digital-only) event kickstarted with a bang. There were a lot of new surprises from the world of SwiftUI, ARKit, PencilKit, Create ML, and Core ML. But the one that stood out for me was computer vision.
Apple’s Vision framework got bolstered with a bunch of exciting new APIs that perform some complex and critical computer vision algorithms in a fairly straightforward way.
Continue reading SwiftUI + Vision Contour Request — Coin Detection in iOS
As the spread of COVID 19 continues, communities are told to reduce close contact between individuals. This is called Social Distancing, as it is a necessary and effective way to slow down the spread of the virus. As a data science student, I came with a solution to identify whether people actually follow the social distancing protocol of staying at least 6 feet from each other.
Continue reading Social Distance Detector with Python, YOLOv4, Darknet, and OpenCV
Data pre-processing is a necessary step before any neural network can successfully ingest and analyze that data. The methods used to do this pre-processing are critical to the network’s performance.
Traditional data pre-processing methods include mean subtraction, normalization, and whitening, which have been around a long time—well before batch normalization came into the picture, which is what we’ll focus on in this post. To start, let’s define these pre-processing methods.
In this post, we’ll have explore the fundamentals of ensemble learning. Specifically, we’ll discuss techniques like bagging, boosting, and stacking in detail and take a brief look at various foundational algorithms that adopt these techniques.
Without further ado, let’s get started!
This article presents techniques for improving classification accuracy by aggregating various classifiers in different ways. These techniques are known as the ensemble methods.
