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.
Revamp that old security camera you have in your backyard — the one that doesn’t even record images! Security systems too costly? Get working on build your own with Twilio and OpenCV !
One of the largest boons of technology has been how, with time, advanced camera and surveillance systems have become so widely accessible. Twilio today has democratized communications channels like voice, text, chat, video, and email by virtualizing the world’s comms infrastructure through APIs that are simple enough for any developer to use, yet robust enough to power the world’s most demanding applications.
Continue reading Remote face detection system with OpenCV and Twilio
This post gives an overview of the different techniques for how to handle categorical variable encoding—i.e. permanently replacing category strings with numerical representations.
Continue reading “Hands-on with Feature Engineering Techniques: Encoding Categorical Variables”
Today, we’re going to be building a RESTFUL API that takes in an image and make predictions using a TensorFlow MobileNet pre-trained model.
TensorFlow.js has many pre-trained models that can be used in projects out of the box. This saves developers the task of training a model from scratch. Here we’re going to explore the MobileNet pre-trained architecture
