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Déploiement sur du hardware
Déployez des workflows de Deep Learning et de Machine Learning sur du hardware.
Sélection d՚exemples
Classify ECG Signals Using DAG Network Deployed to FPGA
Classify human electrocardiogram (ECG) signals by deploying a trained directed acyclic graph (DAG) network.
Code Generation for a Deep Learning Simulink Model to Classify ECG Signals
Create and deploy a Simulink® model for signal classification using wavelet-based features.
Deep Learning Code Generation on ARM for Fault Detection Using Wavelet Scattering and Recurrent Neural Networks
Perform acoustic-based fault detection on a Raspberry Pi using wavelet scattering and recurrent neural networks.
Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning
Generate and deploy a CUDA® executable to classify electrocardiogram signals using wavelet-derived features.
Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi
Classify human electrocardiogram signals on a Raspberry Pi using scalograms and a deep convolutional neural network.
Modulation Classification Using Wavelet Analysis on NVIDIA Jetson
Use wavelets to classify waveforms on a NVIDIA Jetson®.
