youtube links to the transformer video that is helpful for
understanding the transformer:
https://www.youtube.com/watch?v=wjZofJX0v4M
https://www.youtube.com/watch?v=eMlx5fFNoYc
3Blue1Brown. (2024, April 1). Transformers, the tech behind LLMs | Deep Learning Chapter 5 [Video]. YouTube.
https://www.youtube.com/watch?v=wjZofJX0v4M
Attention in transformers, step-by-step | Deep Learning Chapter 6
http://mcb111.org/w06/w06-lecture.html
Has some nice textbook downloads – e.g.
http://mcb111.org/w06/KollerFriedman.pdf
QT:{{”
There are many good books to learn about probabilistic models. “Probabilistic graphical models: principles and techniques” (by Koller & Friedman) is a comprehensive source about more general probabilistic models than the one we are going to study here.
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Subset of chap 7 focuses on GMRF
course website
https://www.biostat.wisc.edu/bmi776/
and https://www.biostat.wisc.edu/bmi776/syllabus.html
canvas for intro bioinformatics. This was the previous website in 2017, https://www.biostat.wisc.edu/bmi576/syllabus.html
“double descent”
The book with a nice explanation of suffix array and BWT is
Bioinformatics Algorithms: An Active Learning Approach by Phillip Compeau & Pavel Pevzner. https://www.bioinformaticsalgorithms.org/
Whalen, S., Schreiber, J., Noble, W. S., & Pollard, K. S. (2021). Navigating the pitfalls of applying machine learning in genomics. Nature Reviews Genetics, 23(3), 169–181.
https://doi.org/10.1038/s41576-021-00434-9
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