2.Edge_AI_Fundamentals_with_OpenVINO
3.Choosing_the_Right_Hardware
4.Optimization_Techniques_and_Tools
A Survey on Methods and Theories of Quantized Neural Networks.pdf
Ananalysis of Deep Neural Network Models for Practical Applications.pdf
Compressing Neural Networks with the Hashing Trick.pdf
Deep Compression Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.pdf
Deep Residual Learning for Image Recognition.pdf
Designing Energy-Efficient Convolutional Neural Networksusing Energy-Aware Pruning.pdf
Distilling the Knowledge in a Neural Network.pdf
Faster Gaze Prediction With Dense Networksand Fisher Pruning.pdf
Faster R-CNN-Towards Real-Time ObjectDetection with Region Proposal Networks.pdf
Going deeper with convolutions.pdf
Learning both Weights and Connections for EfficientNeural Networks.pdf
MobileNets-Efficient Convolutional Neural Networks for Mobile Vision Applications.pdf
Model compression via distillation and quantization.pdf
SSD-Single Shot MultiBox Detector.pdf
Self-training with Noisy Student improves ImageNet classification.pdf
XNOR-Net ImageNet Classification Using BinaryConvolutional Neural Networks.pdf
Xception Deep Learning with Depthwise Separable Convolutions.pdf
You Only Look Once-Unified, Real-Time Object Detection.pdf
Intel+Edge+AI+for+IoT+Developers+Nanodegree+Program+Syllabus.pdf
Intel-Scholarship-Winner-Badge.jpg
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