started writing my own documentation

Author: Johanmkr

.gitignore

@@ -21,6 +21,8 @@ local*
 formatting.x 
 testrun.x
 storage/
+Makefile
+job.yaml
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

doc/Johan_page.md

@@ -1,22 +1,22 @@
 # Individual task for Johan
 
-## My implementation tasks 
+## My implementation tasks
 
 * Data: Implement [MNIST](../CollaborativeCoding/dataloaders/mnist_4_9.py) dataset with digits between 4-9.
-* Model: [MLP-model](../CollaborativeCoding/models/johan_model.py/) with 4 hidden layers, each with 77 neurons and ReLU activation.
+* Model: [MLP-model](../CollaborativeCoding/models/johan_model.py) with 4 hidden layers, each with 77 neurons and ReLU activation.
 * Evaluation metric: [Precision](../CollaborativeCoding/metrics/precision.py).
 
 ## Implementation choices
 
-### Dataset 
+### Dataset
 
-The choices regarding the dataset were mostly done in conjunction with Jan (@hzavadil98) as we were both using the MNIST dataset. Jan had the idea to download the binary files and construct the images from those. The group decided collaboratorily to make the package download the data once and store it for all of use to use. Hence the individual implementations are fairly similar, at least for the two MNIST dataloaders. Were it not for these individual tasks, there would have been one dataloader class, initialised with two separate ranges for labels 0-3 and 4-9. However, individual dataloaders had to be created to comply with the exam description. For my implementation, the labels had to be mapped to a range starting at 0: $(4-9) \to (0,5)$ since the  cross-entropy loss function in PyTorch expect this range. 
+The choices regarding the dataset were mostly done in conjunction with Jan (@hzavadil98) as we were both using the MNIST dataset. Jan had the idea to download the binary files and construct the images from those. The group decided collaboratively to make the package download the data once and store it for all of use to use. Hence, the individual implementations are fairly similar, at least for the two MNIST dataloaders. Were it not for these individual tasks, there would have been one dataloader class, initialised with two separate ranges for labels 0-3 and 4-9. However, individual dataloaders had to be created to comply with the exam description. For my implementation, the labels had to be mapped to a range starting at 0: $(4-9) \to (0,5)$ since the cross-entropy loss function in PyTorch expect this range.
 
-## Experiences with running someone else's code 
+## Experiences with running someone else's code
 
-## Experiences having someone else to run my code 
+## Experiences having someone else to run my code
 
-## I learned how to use these tools during this course 
+## I learned how to use these tools during this course
 
 ### Git-stuff
 
@@ -26,8 +26,10 @@ The choices regarding the dataset were mostly done in conjunction with Jan (@hza
 
 ### Proper documentation
 
-### Nice ways of testing code 
+### Nice ways of testing code
 
 ### UV
 
+## General thoughts on collaboration
 
+As someone new to this University and how the IT-setup is here, this project was very fruitful. The positive thing about working with peers that are highly skilled in the relevant field is that the learning curve is steep, and the take home for me was significant. The con is that I constantly felt behind, spend half the time just understanding the changes implemented by others and felt that my contributions to the overall project was less significant. However, working with skilled peers have boosted my understanding about how things work around here, especially git (more fancy commands that add, commit, push and pull) and docker, cluster operations, kubernetes and logging metrics on Weights and Biases.