diff --git a/nbs/2. Topic Modeling with NMF and SVD.ipynb b/nbs/2. Topic Modeling with NMF and SVD.ipynb
index 9b09ca1..7182fd9 100644
--- a/nbs/2. Topic Modeling with NMF and SVD.ipynb	
+++ b/nbs/2. Topic Modeling with NMF and SVD.ipynb	
@@ -100,13 +100,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "- [Data source](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html): Newsgroups are discussion groups on Usenet, which was popular in the 80s and 90s before the web really took off.  This dataset includes 18,000 newsgroups posts with 20 topics.\n",
+    "- [Data source](https://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html#loading-the-20-newsgroups-dataset): Newsgroups are discussion groups on Usenet, which was popular in the 80s and 90s before the web really took off.  This dataset includes 18,000 newsgroups posts with 20 topics.\n",
     "- [Chris Manning's book chapter](https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf) on matrix factorization and LSI \n",
     "- Scikit learn [truncated SVD LSI details](http://scikit-learn.org/stable/modules/decomposition.html#lsa)\n",
     "\n",
     "### Other Tutorials\n",
-    "- [Scikit-Learn: Out-of-core classification of text documents](http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html): uses [Reuters-21578](https://archive.ics.uci.edu/ml/datasets/reuters-21578+text+categorization+collection) dataset (Reuters articles labeled with ~100 categories), HashingVectorizer\n",
-    "- [Text Analysis with Topic Models for the Humanities and Social Sciences](https://de.dariah.eu/tatom/index.html): uses [British and French Literature dataset](https://de.dariah.eu/tatom/datasets.html) of Jane Austen, Charlotte Bronte, Victor Hugo, and more"
+    "- [Scikit-Learn: Out-of-core classification of text documents](http://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html): uses [Reuters-21578](https://archive.ics.uci.edu/dataset/137/reuters+21578+text+categorization+collection) dataset (Reuters articles labeled with ~100 categories), HashingVectorizer\n",
+    "- [Text Analysis with Topic Models for the Humanities and Social Sciences](https://github.com/DARIAH-DE/tatom): uses [British and French Literature dataset](https://github.com/DARIAH-DE/tatom/tree/develop/data) of Jane Austen, Charlotte Bronte, Victor Hugo, and more"
    ]
   },
   {
@@ -418,7 +418,7 @@
     "The SVD algorithm factorizes a matrix into one matrix with **orthogonal columns** and one with **orthogonal rows** (along with a diagonal matrix, which contains the **relative importance** of each factor).\n",
     "\n",
     "<img src=\"images/svd_fb.png\" alt=\"\" style=\"width: 80%\"/>\n",
-    "(source: [Facebook Research: Fast Randomized SVD](https://research.fb.com/fast-randomized-svd/))\n",
+    "(source: [Facebook Research: Fast Randomized SVD](https://research.facebook.com/blog/2014/9/fast-randomized-svd/))\n",
     "\n",
     "SVD is an **exact decomposition**, since the matrices it creates are big enough to fully cover the original matrix. SVD is extremely widely used in linear algebra, and specifically in data science, including:\n",
     "\n",