Neural networks are types of machine learning algorithms that are everywhere in our daily lives! We hear a lot about them in the news today, but how do they work? This episode introduces some neural network basics, and uses this information to build an autoencoder, a type of neural network that automatically learns how to compress and decompress images, and can even be used to generate new images that did not exist before!
The autoencoder shown in this video is implemented in Python on the Jetson Nano, a small single board computer with an integrated GPU made by NVIDIA! Built for embedded applications, this little board is a machine learning powerhouse and makes it easy to integrate neural networks into your own DIY maker projects!
Ready to continue learning about neural networks or get started on implmenting your own? Check out the links below for more details!
More info about the NVIDIA Jetson Nano:
NVIDIA Developer Installation Instructions:
Autoencoder Jupyter Notebook:
Interested in setting up your own Jetson Nano? You can pick up all of the hardware used in this video from Amazon using the following affiliate links:
NVIDIA Jetson Nano Developer Kit:
5V 4A Power Supply:
Noctua 40x20mm 5V PWM Fan:
Belkin N300 Pocket USB WiFi Adapter:
Logitech K400 Wireless Keyboard and Mouse Combo:
Samsung EVO Select 32GB SD Card with Reader:
Raspberry Pi Camera Module V2-8 Megapixel, 1080p:
AGPTEK HD 1080p Video Capture HDMI Recorder:
Fosmon 3-Port HDMI Switch:
SanDisk Ultra 32GB USB Flash Drive:
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