The power of AI is now in the hands of makers, self-taught developers, and embedded technology enthusiasts everywhere with the PRISMASENSE Nano Developer Kit. This easy-to-use, powerful computer lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. In this course, you’ll use Jupyter iPython notebooks on your own Jetson Nano to build a deep learning classification project with computer vision models.
You’ll learn how to:
- Set up your Jetson Nano and camera
- Collect image data for classification models
- Annotate image data for regression models
- Train a neural network on your data to create your own models
- Run inference on the Jetson Nano with the models you create
Upon completion, you’ll be able to create your own deep learning classification and regression models with the Jetson Nano.
Prerequisites: Basic familiarity with Python (helpful, not required)
Tools, libraries, frameworks used: PyTorch, Jetson Nano
Assessment Type: Multiple-choice
- PRISMASENSE Jetson Nano Developer Kit
- High-performance microSD card: 32GB minimum (we’ve tested and recommend this one)
- 5V 4A power supply with 2.1mm DC barrel connector (we’ve tested and recommend this one)
- 2-pin jumper: must be added to the Jetson Nano Developer Kit board to enable power from the barrel jack power supply (here’s an example)
- Logitech C270 USB Webcam (we’ve tested and recommend this one).
- USB cable: Micro-B To Type-A with DATA enabled (we’ve tested and recommend this one)
Additional Computer Requirements
- A computer with an internet connection and the ability to flash your microSD card
- An available USBA port on your computer (you may need an adapter if you only have USBC ports)