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Microsoft Vision AI Developer Kit Simplifies Building Vision-Based Deep Learning Projects

#artificialintelligence

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.


Microsoft Vision AI Developer Kit Simplifies Building Vision-Based Deep Learning Projects

#artificialintelligence

For the Vision AI Developer Kit, Microsoft and Qualcomm have partnered to simplify training and deploying computer vision-based AI models. Developers can use Microsoft's cloud-based AI and IoT services on Azure to train models while deploying them on the smart camera edge device powered by a Qualcomm's AI accelerator. Let's take a close look at Vision AI Developer Kit. The Vision AI Developer Kit not only looks stylish and sophisticated, but also boasts of an impressive configuration. The kit is powered by a Qualcomm Snapdragon 603 processor, 4GB of LDDR4X memory and 16GB of eMMC storage.


Microsoft's Azure IoT Edge, now generally available, is key to Redmond's IoT strategy

ZDNet

Microsoft's Azure IoT Edge service is generally available (GA) globally, as of today, June 27. Azure IoT Edge is a service which allows users to deploy and run Azure services, AI and custom logic on IoT devices. Users can containerize Azure Cognitive Services, Machine Learning, Stream Analytics and Functions so they can run them on all kinds of devices, ranging from Raspberry Pis to industrial equipment. This is what Microsoft means when it talks about processing data "at the edge." For the couple of years, Microsoft has been refocusing its mission around tools and services for the "intelligent cloud and intelligent edge."


PyTorch or TensorFlow?

@machinelearnbot

This is a guide to the main differences I've found between PyTorch and TensorFlow. This post is intended to be useful for anyone considering starting a new project or making the switch from one deep learning framework to another. The focus is on programmability and flexibility when setting up the components of the training and deployment deep learning stack. I won't go into performance (speed / memory usage) trade-offs. PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects.


PyTorch or TensorFlow?

@machinelearnbot

This is a guide to the main differences I've found between PyTorch and TensorFlow. This post is intended to be useful for anyone considering starting a new project or making the switch from one deep learning framework to another. The focus is on programmability and flexibility when setting up the components of the training and deployment deep learning stack. I won't go into performance (speed / memory usage) trade-offs. PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects.