[add] hw5b, updated grad proj paths

.tool-versions

@@ -1 +1 @@
-ruby 3.3.0
+ruby 3.3.4

_modules/week-06.md

@@ -19,4 +19,4 @@ Oct 3
 
 Oct 4
 : **Lab 5**{: .label .label-lab }  [Modeling, Summary Statistics, and Loss Functions](https://data100.datahub.berkeley.edu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2FDS-100%2Ffa24-student&urlpath=lab%2Ftree%2Ffa24-student%2Flab%2Flab05%2Flab05.ipynb&branch=main) (due Oct 8)
-: **Homework 5A**{: .label .label-hw } [Modeling](https://drive.google.com/file/d/15HiqqwDwImzfIvNnotS4ITgUMQyGioUM/view) (due Oct 10)
+: **Homework 5A**{: .label .label-hw } [Modeling](https://drive.google.com/file/d/15HiqqwDwImzfIvNnotS4ITgUMQyGioUM/view){:target="_blank"} (due Oct 10)

_modules/week-07.md

@@ -18,4 +18,4 @@ Oct 10
 
 Oct 11
 : **Lab 6**{: .label .label-lab }  OLS (due Oct 15)
-: **Homework 5B**{: .label .label-hw } OLS (due Oct 17)
+: **Homework 5B**{: .label .label-hw } [OLS](https://drive.google.com/file/d/1rAT7zcbLT8tU7VSCe1u98LpvWG7MsjPJ/view?usp=sharing){:target="_blank"} (due Oct 17)

gradproject-cv.md

@@ -266,7 +266,7 @@ The presentation video should provide an overview of your project, highlighting
 
 This component of the final report will be graded based on your models' performance on the test set for both tasks of your chosen project (please find the thresholds for each task in the rubrics). You will need to submit your predictions to Gradescope to get your test performance; you will be allowed to upload up to 4 times a day. Please find the test data for each task below.
 
-`test_images_flooding-fire.npz` is the test for Task A and `test_images_hurricane-matthew.npz` is the test set for Task B; both files are located in `data100-shared-readwrite/fa24_grad_project_data/satellite-image-data` directory on Data 100 Datahub.
+`test_images_flooding-fire.npz` is the test for Task A and `test_images_hurricane-matthew.npz` is the test set for Task B; both files are located in `shared/course/data100-shared-readwrite/fa24_grad_project_data/satellite-image-data` directory on Data 100 Datahub.
 
 Please submit two CSV files named `test_images_flooding-fire_predictions.csv`, and `test_images_hurricane-matthew_predictions.csv` to the Gradescope assignment (assignment link coming soon). Each CSV should only contain one column named `pred`.
 

gradproject-nlp.md

@@ -286,7 +286,7 @@ The test sets for both tasks are named as follows:
 4. `arena-validation-set-model_b_response-embeddings.npy`
 5. `arena-validation-set-topic-modeling.jsonl.gz`
 
-All files are located in `data100-shared-readwrite/fa24_grad_project_data/nlp-chatbot-analysis_data/validation-set` directory on Data 100 Datahub.
+All files are located in `shared/course/data100-shared-readwrite/fa24_grad_project_data/nlp-chatbot-analysis_data/validation-set` directory on Data 100 Datahub.
 
 Submit a single CSV file with three columns named `question_id`, `winner`, `hardness_score` to the Gradescope assignment (assignment link coming soon).
 

gradproject.md

@@ -41,7 +41,7 @@ For each milestone listed above, detailed expectations can be found in the "Mile
 
 ## Accessing Datasets
 
-All of the provided datasets can be found in the Datahub directory `data100-shared-readwrite/fa24_grad_project_data`. You can access the data directly from Datahub. If you wish to work on the project locally, you can also download the files containing the datasets for each topic by right-clicking on the file in JupyterLab and select "Copy Download Link". If you choose to train more complex models, DataHub might not have enough hardware resources or memory, in which case you can use [Google Colab](https://colab.google/){:target="_blank"} or your local machine. If you would like to use Google Colab, feel free to check out this [link](https://stackoverflow.com/questions/48376580/how-to-read-data-in-google-colab-from-my-google-drive){:target="_blank"} to get started.
+All of the provided datasets can be found in the Datahub directory `shared/course/data100-shared-readwrite/fa24_grad_project_data`. You can access the data directly from Datahub. If you wish to work on the project locally, you can also download the files containing the datasets for each topic by right-clicking on the file in JupyterLab and select "Copy Download Link". If you choose to train more complex models, DataHub might not have enough hardware resources or memory, in which case you can use [Google Colab](https://colab.google/){:target="_blank"} or your local machine. If you would like to use Google Colab, feel free to check out this [link](https://stackoverflow.com/questions/48376580/how-to-read-data-in-google-colab-from-my-google-drive){:target="_blank"} to get started.
 
 ## Project Topics