Update README.md

Author: eitansela

README.md

@@ -55,7 +55,7 @@ The repository contains the following resources:
  - [**TensorFlow Script Mode Debug Training Script**](tensorflow_script_mode_debug_local_training): This example shows how to debug your training script running inside a prebuilt SageMaker Docker image for TensorFlow, on your local machine using SageMaker local mode.
  - [**TensorFlow Script Mode Deploy a Trained Model and inference on file from S3**](tensorflow_script_mode_local_model_inference): This example shows how to deploy a trained model to a SageMaker endpoint, on your local machine using SageMaker local mode, and inference with a file in S3 instead of http payload for the SageMaker Endpoint.
  - [**TensorFlow Script Mode Training and Batch Transform**](tensorflow_script_mode_california_housing_local_training_and_batch_transform): This example shows how to train your model and run Batch Transform job with TensorFlow and SageMaker script mode, on your local machine using SageMaker local mode.
- - [**TensorFlow Extend AWS TensorFlow Deep Learning Container Image**](tensorflow_extend_dlc_california_housing_local_training): In this example we show how to package a TensorFlow container, extending the SageMaker TensorFlow container, with a Python example which works with the California Housing dataset. By extending the SageMaker TensorFlow container we can utilize the existing training solution made to work on SageMaker, leveraging SageMaker TensorFlow `Estimator` object, with `entry_point` parameter, specifying your local Python source file which should be executed as the entry point to training. To make it work, we replace the `framework_version` and `py_version` parameters, with `image_uri` of the Docker Image we have created. 
+ - [**TensorFlow extend AWS TensorFlow Deep Learning Container Image**](tensorflow_extend_dlc_california_housing_local_training): In this example we show how to package a TensorFlow container, extending the SageMaker TensorFlow container, with a Python example which works with the California Housing dataset. By extending the SageMaker TensorFlow container we can utilize the existing training solution made to work on SageMaker, leveraging SageMaker TensorFlow `Estimator` object, with `entry_point` parameter, specifying your local Python source file which should be executed as the entry point to training. To make it work, we replace the `framework_version` and `py_version` parameters, with `image_uri` of the Docker Image we have created. 
 
 - **PyTorch resources:**