Using Python 3.6, OpenCV, Dlib and the face_recognition module

The new Nvidia Jetson Nano 2GB dev board (announced today!) is a single-board computer that goes for $59 and runs AI software with GPU-acceleration.

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With face recognition and python, you can easily track everyone who creeps up to your door.

The kind performance you can get out of a $59 single-board computer in 2020 is kind of amazing. Let’s use it to create a simple version of a doorbell camera that tracks everyone that walks up to the front door of your house. With face recognition, it will instantly know whether the person at your door has ever visited you before — even if they were dressed differently.

What is the Nvidia Jetson Nano 2GB?

The Jetson Nano 2GB is a single-board computer with a quad-core 1.4ghz ARM CPU and an Nvidia Maxwell GPU built-in. It’s the cheapest Nvidia Jetson model and aimed at the same kind of hobbyists who would be buying a Raspberry Pi. …


Natural Language Processing is Fun! Part 5

This article is part of an on-going series on NLP: Part 1, Part 2, Part 3, Part 4, Part 5.

In the last few years, there has been a revolution in machine translation. New translation systems built using deep learning have replaced older systems built by linguists using decades of research in statistics. Popular translation products like Google Translate have switched their internals out and replaced their old code with new deep learning models.

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Real output from the Spanish-to-English translation model that we’ll train

This is great for everyone. The deep learning approach is not only more accurate, but it doesn’t require you to have a graduate degree in linguistics to understand. In theory, it’s simple enough that anyone with enough training data and computer power should be able to build their own language translation system. Despite this, it’s still been difficult to build a translation system on your own because the amount of data and computing power required was prohibitive for the average bedroom hobbyist. …


You can read a reader-translated version of this article in فارسی.

Machine learning and data science tools are more accessible than ever. But along with learning the tools themselves, it’s just as important to learn how to effectively explore data and figure out its limitations before you feed the data into your modeling tools.

You’d be surprised how often people jump into building models without looking at the data. This is a mistake. …

About

Adam Geitgey

Interested in computers and machine learning. Likes to write about it.

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