GitHub - priyanka36/Inferencing-on-Edge-Devices: Model deployment on edge devices is one of the most challenging task in AI.Quantization can help us perform inferences on edge devices.There are two types of quantization training aware quantization and post training quanitization.

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Model deployment on edge devices is one of the most challenging task in AI.Quantization can help us perform inferences on edge devices.There are two types of quantization training aware quantization and post training quanitization.

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Name		Name	Last commit message	Last commit date
Latest commit History 10 Commits
CORAL_TPU_FOR_INFERENCE.pdf		CORAL_TPU_FOR_INFERENCE.pdf
ChallengesOFInferencingOnEdgeDevices.pdf		ChallengesOFInferencingOnEdgeDevices.pdf
ConvertTensorFLowModelToTFliteModel.pdf		ConvertTensorFLowModelToTFliteModel.pdf
Inferencing On TensorflowLite.pdf		Inferencing On TensorflowLite.pdf
NVIDIA.pdf		NVIDIA.pdf
OnlineMachineLearning.pdf		OnlineMachineLearning.pdf
PostTrainingQuantization.ipynb		PostTrainingQuantization.ipynb
PostTrainingQuantizationEdgeDevices.ipynb		PostTrainingQuantizationEdgeDevices.ipynb
QuantizationAwareTraining.ipynb		QuantizationAwareTraining.ipynb
SettingUpTPU.pdf		SettingUpTPU.pdf
TensorflowInstallation.pdf		TensorflowInstallation.pdf
quantizationawaretraining.py		quantizationawaretraining.py