Merge overleaf-2023-06-05-1722 into main

embeddings.tex

@@ -267,7 +267,7 @@ \section{Introduction}
 \begin{figure}[H]
 \centering
 \includegraphics[width=.7\linewidth]{figures/embeddings_1.png}
-\caption{Embeddings papers in Arxiv by month. It's interesting to note the decline in frequency of embeddings-specific papers, possibly in tandem with the rise of deep learning architectures like GPT \href{https://github.com/veekaybee/embeddings_code/blob/main/fig_2_embeddings_papers.ipynb}{source}}
+\caption{Embeddings papers in Arxiv by month. It's interesting to note the decline in frequency of embeddings-specific papers, possibly in tandem with the rise of deep learning architectures like GPT \href{https://github.com/veekaybee/what_are_embeddings/blob/main/notebooks/fig_2_embeddings_papers.ipynb}{source}}
 \end{figure}
 
 Building and expanding on the concepts in Word2Vec, the Transformer  \citep{vaswani2017attention} architecture, with its self-attention mechanism, a much more specialized case of calculating context around a given word, has become the de-facto way to learn representations of growing multimodal vocabularies, and its rise in popularity both in academia and in industry has caused embeddings to become a staple of deep learning workflows.