I recently worked through a great video by Nicholas Renotte on how to use Tensorflow2 Object Detection API for sign language.
Using Tensorflow 2, I re-created that project and you can read about that on my Github repo.
During my investigation on how to perform custom object detection, I ran across a Medium blog, “Detect any custom object with DeepStack”.
I was very intrigued by their approach of using Docker containers that either come with pre-configured capability or you can train from your…
Which means using linear machine learning models probably won’t model the real world very well.
This article will take a look at a non-linear data set, concentric circles, and see how we can apply a couple of machine learning and deep learning models to create a classification model.
There are many ways to approach this problem, and this article is not meant to be a complete body of work on the subject, but instead a more casual look at how to approach the problem.
This is not ground breaking data science — it was just a really fun way to make machine learning come alive and make it real. I think this would make a great high school or college level project or a corporate hack-a-thon.
I decided I wanted to try to add a camera to the front of the GoPiGo3 and see if I could use some computer vision and train a machine learning model to have the GoPiGo3 follow a line by predicting, based on the shape of the line, whether the GoPiGo3 should turn left, right or continue straight.
About a month ago I wrote an article about how I performed facial recognition with a single LinkedIn profile picture.
After this post, I had a co-worker ask how Facial Recognition actual works. I tried to explain it, text book style, but I realized that is not very effective. When working with a visual technology, being able to see how all of the parts come together, visually, is really powerful.
So I put together a Jupyter Notebook which you can find on my Github which shows visually each step.
You may have heard about the story in the Washington Post about government agencies are using the data at the Department of Motor Vehicle (DMV) for facial recognition purposes. You can find that article here but when you are done please come back here.
While I understand that is very concerning, we as a social media society leak (firehose?) personal information everyday that is virtually publicly accessible.
I am going to show you how I used a single LinkedIn profile picture from a number of co-workers LinkedIn profiles to create a facial recognition system all using free, open-source tools. The…
The Raspberry PI 4 hit the streets and it is an exciting upgrade. More memory — MUCH more memory and a faster cpu, dual 4k video, and many more features. One of the updates is that now Python 3.7.x is part of the OS distribution. So you do not have to install Python 3.7.
The first thing I wanted to do with the new RaspberryPI 4 was to install OpenCV and my favorite image libraries to perform some facial recognition.
Zappa.io is a great Python tooling for creating AWS serverless applications. From Lambda development, to event handlers, to deploying WSGI applications. You can deploy a Django or Flask application as a Lambda with an API gateway endpoint and never have to worry about configuring an EC2 instance nor scaling or load balancing. Is this suitable for every application — well — no. But for a great number applications it is great.
The tricky part of using Zappa to deploy the application, is when one of the third party libraries that your application depends on has an environment specific implementation. Some…