diff --git a/README.md b/README.md
index cb1c69c..47f3bf0 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,9 @@ This readme explains how the matrices should be used. We also present a simple e
 
 Word embeddings define the similarity between two words by the normalised inner product of their vectors. The matrices in this repository place languages in a single space, **without changing any of these monolingual similarity relationships**. When you use the resulting multilingual vectors for monolingual tasks, they will perform exactly the same as the original vectors.
 
-Note that since we released this repository Facebook have released an additional 204 languages; however the word vectors of the original 90 languages have not changed, and the transformations provided in this repository will still work. 
+Note that since we released this repository Facebook have released an additional 204 languages; however the word vectors of the original 90 languages have not changed, and the transformations provided in this repository will still work. If you would like to learn your own alignment matrices, we provide an example in ["align_your_own.ipynb"](./align_your_own.ipynb).
+
+For a general introduction to word embeddings, we recommend [Colah's blog](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/), or [Sam's introduction to vector representations](https://www.samtalksml.net/from-linear-regression-to-vector-representations/). 
 
 If you use this repository, please cite:
 
@@ -43,7 +45,7 @@ Turns out "chat" and "кот" are pretty similar after all. This is good, since
 ### Ok, so how did you obtain these matrices?
 Of the 89 languages provided by Facebook, 78 are supported by the [Google Translate API](https://cloud.google.com/translate/docs/). We first obtained the 10,000 most common words in the English fastText vocabulary, and then use the API to translate these words into the 78 languages available. We split this vocabulary in two, assigning the first 5000 words to the training dictionary, and the second 5000 to the test dictionary.
 
-We described the alignment procedure in the paper. It takes two sets of word vectors and a small bilingual dictionary of translation pairs in two languages; and generates a matrix which aligns the source language with the target. Sometimes Google translates an English word to a non-English phrase, in these cases we average the word vectors contained in the phrase.
+We described the alignment procedure in [this blog](https://www.samtalksml.net/aligning-vector-representations/). It takes two sets of word vectors and a small bilingual dictionary of translation pairs in two languages; and generates a matrix which aligns the source language with the target. Sometimes Google translates an English word to a non-English phrase, in these cases we average the word vectors contained in the phrase.
 
 To place all 78 languages in a single space, **we align every language to the English vectors** (the English matrix is the identity).
 
@@ -190,8 +192,6 @@ Dinu et al., 2014
 6. [Learning principled bilingual mappings of word embeddings while preserving monolingual invariance](https://pdfs.semanticscholar.org/9a2e/ed5f8175275af0d55d4aed39afc8e2b2acf2.pdf?_ga=1.8571637.130713154.1492676520)  
 Artetxe et al., EMNLP 2016
 
-If you would like a general introduction to word embeddings, we recommend [Colah's blog](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/).
-
 ### Training and test dictionaries
 
 A number of readers have expressed an interest in the training and test dictionaries we used in this repository. We would have liked to upload these, however, while we have not taken legal advice, we are concerned that this could be interpreted as breaking the terms of the Google Translate API.