Merge pull request #148 from pkeilbach/winter-term-24-25-preparation-…

…part-2

Winter term 2024/25 preparation part 2

docs/assignments.md

@@ -68,7 +68,7 @@ See the [Getting Started](./getting_started.md) guide for instructions on how to
 
 To submit an assignment, you will need to provide screenshots of a successful test run.
 
-<!-- TODO issue-121 provide example screenshot -->
+![Example of a successful test run](./img/assignment-test-run.png)
 
 You can submit the screenshots via GitHub issue using the issue template for [Assignment Submission](https://github.com/pkeilbach/htwg-practical-nlp/issues/new/choose).
 
@@ -134,22 +134,10 @@ If you don't want to deal with git, you could also work purely locally without c
     When pulling updates, you probably need to [stash](https://git-scm.com/docs/git-stash) your changes.
     But be careful: if not done properly, you may lose your progress! 😱
 
-## Pulling Updates
+## Fetching Updates
 
-As described in the getting started guide, there will be [updates](./getting_started.md#fetching-updates) from time to time.
-It can happen that these updates affect the assignments (just in case you are wondering why your tests suddenly fail 😅).
+As described in the [getting started](./getting_started.md) guide, there will be updates from time to time.
 
-Given that you work on a separate branch on your assignments, you can merge the latest version of the `main` branch into your assignment branch as follows:
+It can happen that these updates affect the assignments - just in case you are wondering why your tests suddenly fail 😅).
 
-```sh
-# Fetch the latest changes from the remote main branch
-git fetch origin main
-
-# Merge the main branch into your current feature branch, e.g. my-assignments
-git merge origin/main
-```
-
-!!! info
-
-    In case you work on a fork (which is awesome 🙌), the process is similar, but you need to fetch from the `upstream` remote repository.
-    This is described in the [contributing guide](https://github.com/pkeilbach/htwg-practical-nlp/blob/main/CONTRIBUTING.md#syncing-you-fork) in more detail.
+So make sure to always [fetch the latest updates](./getting_started.md#fetching-updates) before working on your assignments.

docs/course_profile.md

@@ -92,8 +92,20 @@ My goal is to keep the entry barrier as low as possible!
 
         If you are new to **GitHub** (a popular Git hosting service), you might want to check out [this module](https://learn.microsoft.com/en-us/training/modules/introduction-to-github/).
 
-
-
 !!! tip
 
     In general, [Microsoft Learn](https://docs.microsoft.com/en-us/learn/) offers some great tutorials for all kinds of technologies.
+
+## Literature
+
+Here is a list of recommended literature for this course:
+
+- Bird, Steven, Ewan Klein, and Edward Loper. *Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit*. Sebastopol, CA: O'Reilly Media, 2009. <https://www.nltk.org/book_1ed/>.
+
+- Bishop, Christopher M. *Pattern Recognition and Machine Learning*. New York: Springer, 2006. <https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf>.
+
+- Jurafsky, Daniel, and James H. Martin. *Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition*. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2008. <https://web.stanford.edu/~jurafsky/slp3/>.
+
+- Raschka, Sebastian. *Build a Large Language Model (From Scratch)*. Shelter Island, NY: Manning, 2024. <https://www.manning.com/books/build-a-large-language-model-from-scratch>.
+
+- Vajjala, Sowmya, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana. *Practical Natural Language Processing: A Comprehensive Guide to Building Real-world NLP Systems*. Sebastopol, CA: O'Reilly Media, 2020. <https://www.practicalnlp.ai/>.

tests/htwgnlp/test_embeddings.py

@@ -61,19 +61,35 @@ def test_get_embeddings(loaded_embeddings):
 def test_euclidean_distance(loaded_embeddings, test_vector):
     assert isinstance(loaded_embeddings.euclidean_distance(test_vector), np.ndarray)
     assert loaded_embeddings.euclidean_distance(test_vector).shape == (243,)
-    assert loaded_embeddings.euclidean_distance(test_vector)[0] == 17.507894003796004
-    assert loaded_embeddings.euclidean_distance(test_vector)[1] == 17.76195946823725
-    assert loaded_embeddings.euclidean_distance(test_vector)[42] == 17.787844721963356
-    assert loaded_embeddings.euclidean_distance(test_vector)[242] == 17.745477284490963
+    np.testing.assert_allclose(
+        loaded_embeddings.euclidean_distance(test_vector)[0], 17.507894003796004
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.euclidean_distance(test_vector)[1], 17.76195946823725
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.euclidean_distance(test_vector)[42], 17.787844721963356
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.euclidean_distance(test_vector)[242], 17.745477284490963
+    )
 
 
 def test_cosine_similarity(loaded_embeddings, test_vector):
     assert isinstance(loaded_embeddings.cosine_similarity(test_vector), np.ndarray)
     assert loaded_embeddings.cosine_similarity(test_vector).shape == (243,)
-    assert loaded_embeddings.cosine_similarity(test_vector)[0] == -0.037310105006509546
-    assert loaded_embeddings.cosine_similarity(test_vector)[1] == -0.12679458247346523
-    assert loaded_embeddings.cosine_similarity(test_vector)[42] == -0.026496807469057613
-    assert loaded_embeddings.cosine_similarity(test_vector)[242] == -0.0657470030012723
+    np.testing.assert_allclose(
+        loaded_embeddings.cosine_similarity(test_vector)[0], -0.037310105006509546
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.cosine_similarity(test_vector)[1], -0.12679458247346523
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.cosine_similarity(test_vector)[42], -0.026496807469057613
+    )
+    np.testing.assert_allclose(
+        loaded_embeddings.cosine_similarity(test_vector)[242], -0.0657470030012723
+    )
 
 
 def test_find_closest_word(loaded_embeddings, test_vector):