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Machine Learning 101: Intro To Naive Bayes Classifier (NVIDIA Jetson Xavier NX Review)
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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:
You can pick up your own Jetson Xavier NX here:
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:
You will also need:
💛 Support Me 💛
🛠️ Are you a maker who is looking for a 3D printer? 🛠️
(Note: All Amazon links are affiliate links. As an Amazon Associate I earn from qualifying purchases.)
📷 Videos and Projects Mentioned in the Episode 📷
Comments or questions? Connect with me on social media:
#AI #MachineLearning #JetsonXavierNX
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