Link fixes

docs/learn/programming.md

@@ -7,7 +7,7 @@
 
 # Programming with Vitis AI
 
-Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPU for Alveo, and DPU for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 8 samples in [VART Samples](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/demo/VART) are available to help you get familiar with the unfied programming APIs.
+Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPU for Alveo, and DPU for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 8 samples in [VART Samples](https://github.com/xilinx/vitis-ai/demo/VART) are available to help you get familiar with the unfied programming APIs.
 
 
 | ID | Example Name          | Models              | Framework  | Notes                                                                     |

docs/learn/release_notes.md

@@ -1,4 +1,4 @@
-<table class="sphinxhide">
+<table class="sphinxhide">
  <tr>
    <td align="center"><img src="https://www.xilinx.com/content/dam/xilinx/imgs/press/media-kits/corporate/xilinx-logo.png" width="30%"/><h1>Vitis AI</h1>
    </td>
@@ -122,13 +122,13 @@
 
 
 ### Whole App Acceleration
-- DPUv2 TRD flow to build from sources (see [WAA-TRD](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/dsa/WAA-TRD))
+- DPUv2 TRD flow to build from sources (see [WAA-TRD](https://github.com/xilinx/vitis-ai/dsa/WAA-TRD))
 - DFx based TRD flow to build from pre-built IPs
   1. DPUCZDZ8G
 
-- Existing WAA classification and detection examples ported to DPUv3e (earlier only for DPUv2 and DPUv1) (see [Whole-App-Acceleration](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/demo/Whole-App-Acceleration))
-- Fall Detection App using DPUv1 and Accelerated Optical Flow (see [Fall Detection](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/demo/Whole-App-Acceleration/fall_detection))
-- Detection Post Processing (NMS) Acceleration (see [ssd_mobilenet](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/demo/Whole-App-Acceleration/ssd_mobilenet))
+- Existing WAA classification and detection examples ported to DPUv3e (earlier only for DPUv2 and DPUv1) (see [Whole-App-Acceleration](https://github.com/xilinx/vitis-ai/demo/Whole-App-Acceleration))
+- Fall Detection App using DPUv1 and Accelerated Optical Flow (see [Fall Detection](https://github.com/xilinx/vitis-ai/demo/Whole-App-Acceleration/fall_detection))
+- Detection Post Processing (NMS) Acceleration (see [ssd_mobilenet](https://github.com/xilinx/vitis-ai/demo/Whole-App-Acceleration/ssd_mobilenet))
 
 ### AI Kernel Scheduler
 - Added kernels for new DPUs
@@ -150,7 +150,7 @@
  aiohttp 3.6.3 requires yarl<1.6.0,>=1.0, but you have yarl 1.6.3 which is incompatible.
    - Errors like this can be safely ignored does not affect any Vitis AI functionality
  - Inconsistent accuracies observed in multi-threaded applications using DPUCADF8H
-   - View workaround [here](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/examples/DPUCADF8H/tf_resnet50_multi_thread/scripts).
+   - View workaround [here](https://github.com/xilinx/vitis-ai/examples/DPUCADF8H/tf_resnet50_multi_thread/scripts).
 
 ### Updates
 - v1.3.1

docs/quick-start/install/README.md

@@ -10,7 +10,7 @@
 Two options are available for installing the containers with the Vitis AI tools and resources.
 
  - Pre-built containers on Docker Hub: [xilinx/vitis-ai](https://hub.docker.com/r/xilinx/vitis-ai/tags)
- - Build containers locally with Docker recipes: [Docker Recipes](https://gitenterprise.xilinx.com/Vitis/vitis-ai-staging/tree/1.3.1/setup/docker)
+ - Build containers locally with Docker recipes: [Docker Recipes](https://github.com/xilinx/vitis-ai/setup/docker)
 
  - [Install Docker](install_docker/README.md) - if Docker not installed on your machine yet