Fix: links that lead to page not found (#308)

Author: HyunKyungHan

nvidia_deeplearningexamples_resnet50.md

@@ -31,7 +31,7 @@ The model is initialized as described in [Delving deep into rectifiers: Surpassi
 
 This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time.
 
-Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch)
+Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/triton-inference-server/server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch)
 
 
 ### Example

nvidia_deeplearningexamples_resnext.md

@@ -28,7 +28,7 @@ This model is trained with mixed precision using Tensor Cores on Volta, Turing,
 
 We use [NHWC data layout](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) when training using Mixed Precision.
 
-Note that the ResNeXt101-32x4d model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnext_for_triton_from_pytorch)
+Note that the ResNeXt101-32x4d model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/triton-inference-server/server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnext_for_triton_from_pytorch)
 
 #### Model architecture
 

nvidia_deeplearningexamples_se-resnext.md

@@ -37,7 +37,7 @@ _Image source: [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.0150
 Image shows the architecture of SE block and where is it placed in ResNet bottleneck block.
 
 
-Note that the SE-ResNeXt101-32x4d model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/se_resnext_for_triton_from_pytorch).
+Note that the SE-ResNeXt101-32x4d model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/triton-inference-server/server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/se_resnext_for_triton_from_pytorch).
 
 ### Example