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README.md

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 <img src="https://github.com/dusty-nv/jetson-inference/raw/master/docs/images/deep-vision-header.jpg" width="100%">
 
 # Deploying Deep Learning
-Welcome to our instructional guide for inference and realtime vision [DNN library](#api-reference) for NVIDIA **[Jetson Nano / TX1 / TX2 / Xavier / Orin](https://developer.nvidia.com/embedded-computing)** devices.  This project uses **[TensorRT](https://developer.nvidia.com/tensorrt)** to optimize models for GPUs, and PyTorch for training.
+Welcome to our instructional guide for inference and realtime vision [DNN library](#api-reference) for NVIDIA **[Jetson Nano / TX1 / TX2 / Xavier / Orin](https://developer.nvidia.com/embedded-computing)** devices.  This project uses **[TensorRT](https://developer.nvidia.com/tensorrt)** to run optimized models on GPUs, and PyTorch for training.
 
 Supported DNN vision primitives include [`imageNet`](docs/imagenet-console-2.md) for image classification, [`detectNet`](docs/detectnet-console-2.md) for object detection, [`segNet`](docs/segnet-console-2.md) for semantic segmentation, [`poseNet`](docs/posenet.md) for pose estimation, and [`actionNet`](docs/actionnet.md) for action recognition.  Examples are provided for streaming from live camera feeds and making webapps with WebRTC.  See the **[API Reference](#api-reference)** section for detailed reference documentation of the C++ and Python libraries.