[EN]-AI Tutorial use tensorboard inside a job

Grammatical fixes and a replacement link. I'm not sure the replacement is the correct link but the original resulted in a 404.

Author: SR-OVH-US

pages/public_cloud/ai_machine_learning/training_tuto_05_tensorboard/guide.en-gb.md

@@ -10,7 +10,7 @@ The purpose of this tutorial is to show how it is possible to launch a [TensorBo
 
 TensorBoard is a tool made by TensorFlow, for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.
 
-TensorBoard provides a visual interface :
+TensorBoard provides a visual interface:
 
 ![image](images/overview_tensorboard.png){.thumbnail}
 
@@ -26,11 +26,11 @@ The tutorial presents a simple example of launching **TensorBoard** in a job.
 
 First of all, you must have trained your model and saved your results in an object store container (exemple: `my_tf_metrics` located in Gravelines `GRA`).
 
-Alternatively, you can have a job already `RUNNING` that is plugged with that object store container and is writting metric logs inside it (exemple: `my_tf_metrics@GRA:/runs:RW:cache`). In that last case, don't forget the `cache` parameter indicating that the volume is cached and sharable among jobs. More information about volumes configuration in jobs can be found [here](/pages/public_cloud/ai_machine_learning/cli_12_howto_run_job_cli#attaching-volumes), information about volume caching can be found [here](/pages/public_cloud/ai_machine_learning/gi_02_concepts_data#capabilities).
+Alternatively, you can have a job already `RUNNING` that is connected to that object store container and is writing metric logs inside it (example: `my_tf_metrics@GRA:/runs:RW:cache`). In that last case, don't forget the `cache` parameter indicating that the volume is cached and sharable among jobs. More information about volumes configuration in jobs can be found [here](/pages/public_cloud/ai_machine_learning/cli_12_howto_run_job_cli#attaching-volumes), information about volume caching can be found [here](/pages/public_cloud/ai_machine_learning/gi_02_concepts_data#capabilities).
 
 > [!primary]
 >
-> If you want to see an example of how to use **TensorBoard** to train a model, please refer to this notebook on [GitHub](https://github.com/ovh/ai-training-examples/blob/main/notebooks/tensorflow/tuto/notebook_tutorial_tensorboard.ipynb).
+> If you want to see an example of how to use **TensorBoard** to train a model, please refer to this notebook on [GitHub](https://github.com/ovh/ai-training-examples/blob/main/jobs/tensorboard/train-tensorflow-model.py).
 
 ### Launch TensorBoard in a job
 
@@ -60,7 +60,7 @@ Connect the volume containing your tensorboard metric logs.
 
 > [!primary]
 >
-> `--volume my_tf_metrics@GRA:/runs:RO:cache` indicates that you are connecting the container `my_tf_metrics` from Gravelines (**GRA**) Object Store into the `/runs` directory of your job. The **read only** `RO` permission is enough because TensorBoard does not need access on write. The container `my_tf_metrics@GRA` should contain your tensorflow metrics.
+> `--volume my_tf_metrics@GRA:/runs:RO:cache` indicates that you are connecting the container `my_tf_metrics` from Gravelines (**GRA**) Object Store into the `/runs` directory of your job. The **read only** `RO` permission is enough because TensorBoard does not need access to write. The container `my_tf_metrics@GRA` should contain your tensorflow metrics.
 
 Specify the tensorboard launch command.