Credits

1. “Automatic differentiation from scratch” by Emilio Dorigatti

   When doing research for this textbook, I serendipitously stumbled on this blog. The course banner is inspired by the first graph shown in the blog. It was an amazing reference that helped me decide how I wanted to go about discussing many topics in this textbook.

2. ” The spelled-out intro to neural networks and backpropagation: building micrograd” by Andrej Karpathy

   Andrej Kaparthy’s teaching is well known in the machine learning community, and this textbook wouldn’t have existed without him. When deciding that I wanted to focus on writing Machine Learning resources for Impart Education, I decided to look at how he went about teaching the same material. After seeing that he decided to start his ML series with backpropagation, I decided to as well.

   P.S. it seemed like it’d be a much shorter course compared to anything else, so I also wanted to do it to quickly build up the catalogue on here!

3. “What is Automatic Differentiation?”

   Provided a useful comparison of numerical and symbolic differentiation to automatic differentiation that was used in an attempt to inspire backpropagation.

4. “Chain Rule + Dynamic Programming = Neural Networks”

   Dynamic programming isn’t talked about much when discussing backpropagation, and this blog made me aware of the connection between the two while doing research on backpropagation.