Machine Learning 101: Intro To Naive Bayes Classifier (NVIDIA Jetson Xavier NX Review)

Ғылым және технология

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Image classification is one of the most common applications of Machine Learning today. From facial recognition and tracking in your camera when you take a photo to lane identification in self-driving cars, situations that require a computer to understand what is in an image are all around us. But how exactly does a computer go from raw pixel values to determining whether an object in an image is a dog, a cat, or banana? One way to do it is to use an algorithm called "Gaussian Naive Bayes," a classifier that uses probability to determine what is in an image based on a set of pre-computed features. Gaussian Naive Bayes, also known as GNB, is an incredibly simple but powerful machine learning algorithm that can be used for a variety of tasks.
To explore this topic further, NVIDIA graciously sent me a Jetson Xavier NX, a single board development platform that aims to bring artificial intelligence and machine learning to embedded edge devices. In this video, we set up and code a GNB machine learning algorithm from scratch on the Xavier NX using Python and Jupyter Notebook to classify various types of fruit, so be sure to check it out and learn how to get started with image classification!
Timestamps:
0:00 Intro
1:55 Jetson Xavier NX Setup
5:31 Naive Bayes Explanation
11:00 Classifying Fruit Images with Python
12:13 Results and Analysis
14:04 Outro
Links to code and various websites:
GNB Python Jupyter Notebook: github.com/SuperMakeSomething...
Fruits-360 Image Dataset: github.com/Horea94/Fruit-Imag...
You can learn more about the Jetson Xavier NX here: nvda.ws/3bqcNEx
You can pick up your own Jetson Xavier NX here:
- Amazon US: amzn.to/3eK8lmO
- Amazon DE: amzn.to/2CJNaTv
The Xavier NX's little brother, the Jetson Nano, is another great machine learning platform for makers! You can pick up a Jetson Nano here:
- Amazon US: amzn.to/3g5QiYf
- Amazon DE: amzn.to/3eHKO5Q
You will also need:
HDMI Cable (Amazon US): amzn.to/2HcRIS3
SanDisk Ultra 32GB USB Flash Drive (Amazon US): amzn.to/2YXXk8q
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📷 Videos and Projects Mentioned in the Episode 📷
"Machine Learning 101: Intro To Neural Networks (NVIDIA Jetson Nano Review and Setup)" (Super Make Something) - • What is Multi-Material...
"Sony A6400 Autofocus Test! BETTER Than Dual Pixel Autofocus?!" (The Everyday Dad) - • Sony A6400 Autofocus T...
"Lane Detection and Object Detection with OpenCV & TensorFlow" (Kittipong G) - • Lane detection and obj...
"NVIDIA Jetson: Enabling AI-Powered Autonomous Machines at Scale" (NVIDIA Developer) - • NVIDIA Jetson: Enablin...
"NVIDIA Jetson Xavier NX Review - It's A Beast!" (ETA Prime) - • NVIDIA Jetson Xavier N...
"Robot Brain (NVIDIA Jetson Xavier NX Developer Kit)" (Skyentific) - • Robot Brain (NVIDIA Je...
Comments or questions? Connect with me on social media:
Twitter: / supermakesmthng
Instagram: / supermakesomething
Facebook: / supermakesomething
Twitch: / supermakesomething
#AI #MachineLearning #JetsonXavierNX

Пікірлер: 10

  • @SuperMakeSomething
    @SuperMakeSomething4 жыл бұрын

    *Errata:* I noticed an unfortunate typo on the results slide at 13:12 immediately after uploading the video! The posterior probability for Lime should read *2.18E-11* -- hence the classifier's first choice was an apple and its second choice was a lemon. A value of 2.18E-10 would result in the correct classification of "Lime." Sorry for the confusion!

  • @MrMehshankhan
    @MrMehshankhan3 жыл бұрын

    Thanks for the easy to understand introduction.

  • @Bkry.N
    @Bkry.N4 жыл бұрын

    Great video Learn a lot 👍 ty man

  • @SuperMakeSomething

    @SuperMakeSomething

    4 жыл бұрын

    Pape Demba B. Ndiaye Thank you so much! Glad you liked it and found it useful! 😁

  • @pandagineer1614
    @pandagineer1614 Жыл бұрын

    Great video! I’m new to AI, so I’m wondering: if using your computer had equivalent or superior performance to the Jetson, why use the Jetson? Thanks!

  • @SuperMakeSomething

    @SuperMakeSomething

    Жыл бұрын

    Thanks! These types of devices serve two purposes - 1.) they are great for teaching/learning since the setup is relatively easy, self-contained and cheap (especially considering that it has a GPU, and 2.) they are good for “edge computing” purposes (such as putting it in a robot, for example), where a traditional computer would be too large and/or power hungry. Hope this helps!

  • @JaHaHa7205
    @JaHaHa72053 жыл бұрын

    Great video!

  • @SuperMakeSomething

    @SuperMakeSomething

    3 жыл бұрын

    Thank you, James!

  • @andrewreid9511
    @andrewreid95114 жыл бұрын

    You look like a cyborg with your left eye 😂 the light is soo bright

  • @SuperMakeSomething

    @SuperMakeSomething

    4 жыл бұрын

    🤖🤖🤖

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