Object_Detection (project) - demonstrates training of neural network for object detection on simple dataset. YOLO_GPU (project) - demonstrates the process of accelerating YOLO object detection network for deployment on GPUs. From the link I provided, about two clicks in you can see sample course materials and that may help. Training Courses - National Instruments There are some schools where the NI engineers come and teach courses for free. YOLO_Object_Detection(Cam).vi - demonstrates the process of deploying pretrained network for object detection based on YOLO (You Only Look Once) architecture. Answer (1 of 6): Officially you can get trained by NI applications engineers. MNIST_CNN_GPU (project) - demonstrates the process of accelerating training and deployment on GPUs. MNIST(RT_Deployment) (project) - demonstrates the deploying pretrained model on NI's Real-Time targets. These LabVIEW based final year projects are very helpful for engineering. LabVIEW based electrical projects mainly include real-time projects, industrial automation, controlling, drive, LabVIEW industrial projects etc. LabVIEW programming is used in all the branches of engineering projects like electrical, electronics, IEEE, robotics, Arduino, etc. MNIST_Classifier(Deploy).vi - demonstrates the process of deploying pretrained network by automatically loading network configuration and weights files generated from the examples above. LabView Projects for Engineering Students. MNIST_Classifier_CNN(Train).vi - demonstrates the process of programmatically building and training deep neural networks for image classification task of handwritten digit recognition (based on MNIST dataset) by using CNN (Convolutional Neural Network) architecture
MNIST_Classifier_MLP(Train_1D).vi and MNIST_Classifier_MLP(Train_3D).vi - demonstrates the process of programmatically building and training deep neural networks for image classification task of handwritten digit recognition (based on MNIST dataset) by using MLP (Multilayer Perceptron) architecture.
LabVIEW install path\examples\Ngene\Deep Learning Toolkit Reference examples are part of the toolkit which can be found with the following path: