Update Model Training Gym LP

Author: annietllnd

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/2-devenv.md

@@ -39,6 +39,12 @@ This will:
 - install the `ng-model-gym` package and required dependencies
 - download the datasets and weights needed to run the notebooks
 
+Activate the virtual environment:
+
+```bash
+source nb-env/bin/activate
+```
+
 Run the following in a python shell to confirm that the script was successful:
 
 ```python

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/3-model-training.md

@@ -49,15 +49,15 @@ jupyter lab
 This will prompt you to open your browser to `http://localhost:8888`and enter the token that is printed in the terminal output. Navigate to:
 
 ```output
-neural-graphics-model-gym-examples/model_training_example.ipynb
+neural-graphics-model-gym-examples/tutorials/nss/model_training_example.ipynb
 ```
 
 Step through the notebook for training.
 
 Once your model is trained, the next step is evaluation. You'll measure accuracy, compare checkpoints, and prepare the model for export. Open the evaluation notebook located at the following location:
 
 ```output
-neural-graphics-model-gym-examples/model_evaluation_example.ipynb
+neural-graphics-model-gym-examples/tutorials/nss/model_evaluation_example.ipynb
 ```
 
 At the end you should see a visual comparison of the NSS upscaling and the ground truth image.

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/4-model-explorer.md

@@ -8,8 +8,6 @@ layout: learningpathall
 
 ## What is Model Explorer?
 
-TODO: verify .vgf flavor runs smoothly
-
 Model Explorer is a visualization tool for inspecting neural network structures and execution graphs. Arm provides a VGF adapter for Model Explorer, allowing you to visualize `.vgf` models created from your training and export pipeline.
 
 This lets you inspect model architecture, tensor shapes, and graph connectivity before deployment. This can be a powerful way to debug and understand your exported neural graphics models.