Face recognition with OpenCV, Python, and deep learning

In today’s blog post you are going to learn how to perform face recognition in both images and video streams using:

  • OpenCV
  • Python
  • Deep learning

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

To learn more about face recognition with OpenCV, Python, and deep learning, just keep reading!



AWS DeepLens: first impressions + tutorial

Getting up-and-running with Amazon’s new machine learning-enabled camera

tl;dr It’s awesome. Get one.

At the end of 2017, Amazon announced DeepLens, a camera with specialized hardware that allows developers to deploy machine learning and computer vision models to “the edge,” and integrate the data it collects with other AWS services.

On a whim, I put in a one-click order on Prime (devices started shipping just last week); it arrived a couple days later and just hours from unboxing — with one or two minor hiccups — I got it up-and-running and integrated with other AWS services. I’ve been pleasantly surprised, to say the least.


How To Scrape Web Pages with Beautiful Soup and Python 3

Many data analysis, big data, and machine learning projects require scraping websites to gather the data that you’ll be working with. The Python programming language is widely used in the data science community, and therefore has an ecosystem of modules and tools that you can use in your own projects. In this tutorial we will be focusing on the Beautiful Soup module.

Beautiful Soup, an allusion to the Mock Turtle’s song found in Chapter 10 of Lewis Carroll’s Alice’s Adventures in Wonderland, is a Python library that allows for quick turnaround on web scraping projects. Currently available as Beautiful Soup 4 and compatible with both Python 2.7 and Python 3, Beautiful Soup creates a parse tree from parsed HTML and XML documents (including documents with non-closed tags or tag soup and other malformed markup).

In this tutorial, we will collect and parse a web page in order to grab textual data and write the information we have gathered to a CSV file.


Python Application Layouts: A Reference

Python, though opinionated on syntax and style, is surprisingly flexible when it comes to structuring your applications.

On the one hand, this flexibility is great: it allows different use cases to use structures that are necessary for those use cases. On the other hand, though, it can be very confusing to the new developer.

The Internet isn’t a lot of help either—there are as many opinions as there are Python blogs. In this article, I want to give you a dependable Python application layout reference guide that you can refer to for the vast majority of your use cases.

You’ll see examples of common Python application structures, including command-line applications (CLI apps), one-off scripts, installable packages, and web application layouts with popular frameworks like Flask and Django.

Reverse engineering AWS Lambda

So I have been spending some time jamming my hands into AWS Lambda’s greasy internals, and I’d like to share all the wonderful details I’ve discovered.

why though?

I’ve use AWS Lambda quite extensively at work. And I wanted to get a better understanding of its inner working. What prompted this, you might ask?

Unofficial Native Go Runtime for Google Cloud Functions

There was an off handed comment by the author about the “Lambda API being a bit more complex.”

Well I aim to find out just how complex it is, with the end goal of writing a custom runtime, similar to the one above.

Probably in Python, just because it’s quick to prototype with.

Lets get started, shall we?


For the impatient of you, if you just want to see the results, feel free to look at the code here.