Use MATLAB to Train on Jetson Nano: Unterschied zwischen den Versionen
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*Deep Learning with MATLAB | *Deep Learning with MATLAB | ||
#The fundamental of training an AI system is called deep learning. MATLAB provides a variety of helpful toolboxes, pre-trained networks, and tools to process data into useful input for an AI [https://de.mathworks.com/videos/search.html?q=&fq%5B%5D=product:GC&page=1]. | #The fundamental of training an AI system is called deep learning. MATLAB provides a variety of helpful toolboxes, pre-trained networks, and tools to process data into useful input for an AI [https://de.mathworks.com/videos/search.html?q=&fq%5B%5D=product:GC&page=1]. | ||
#The training can be stored as a model, usually in a .MAT format, which can be | #The training can be stored as a model, usually in a .MAT format, which can be reused. Hence, we do not have to train a model every time we want to use it. | ||
*Gpu Coder | *Gpu Coder | ||
Version vom 1. Juni 2025, 13:48 Uhr
Setup
Overview
To train the AI using MATLAB, I will write a script on MATLAB and then use GPU Coder to flash the function/model on the JetRacer. So all the debugging process will be on MATLAB.
Key Concepts
- Deep Learning with MATLAB
- The fundamental of training an AI system is called deep learning. MATLAB provides a variety of helpful toolboxes, pre-trained networks, and tools to process data into useful input for an AI [1].
- The training can be stored as a model, usually in a .MAT format, which can be reused. Hence, we do not have to train a model every time we want to use it.
- Gpu Coder
- GPU Coder generates optimized CUDA® C++ from your MATLAB® algorithms, unlocking the parallel compute power of the Jetson GPU for real-time processing on your JetRacer and improve 40 times the performance on the jetracer [2][3].
- Helps target GPUs for automotive applications.