Batch Name : Embedded Edge AI Computing
Duration : 2-3 hr per lecture session, 1-2 times/week for 2 and a half weeks
Core Coding Topics:
(1) Development toolchain, e.g., jetpack and OS source distribution for Nvidia embedded platforms, Jetson NANO and Tx2;
(2) OpenCV, Python, TF, Pytorch for Deep Learning with emphasis on IP video streaming and yolo4;
(3) Program/performance profiling, Hardware Architecture, GPU and its optimization;
(4) Cuda Programming and debugging tools.
(5) Unity AI platform, C# and interface to TF and other Deep Learning package.
(6) Mathematical foundation in Deep Reinforcement Learning.
 
Embedded Edge AI computing, Nividia Jetson NANO and Tx2; OpenCV, Python, TF, Pytorch for Deep Learning, IP video streaming, PTZ CAM control, and yolo4; TF Program/performance profiling, Hardware Architecture, GPU and its optimization; Cuda Programming and debugging tools. Unity AI platform, C# and interface to TF and other Deep Learning package. Mathematical foundation in Deep Reinforcement Learning.
 
Prerequsite:
Python, full time engineer interns.