Posts Tagged ‘cbb752b22’

Google Colab intro/resources

August 29, 2021

Here is a google colab notebook that runs you through the basics of using colab notebooks:
https://colab.research.google.com/notebooks/basic_features_overview.ipynb

This one is a comprehensive basic python tutorial. One can learn python without reading a book, or even installing python on your own system (good for someone who knows basic programming, but not python language).
https://colab.research.google.com/github/cs231n/cs231n.github.io/blob/master/python-colab.ipynb

Maybe the most impressive thing you can run on google colab now is the AlphaFold2 code, fold any protein for free.
https://colab.research.google.com/github/deepmind/alphafold/blob/main/notebooks/AlphaFold.ipynb

Michael Levitt on Twitter: “Need advice on good Python books & courses. Learned FORTRAN from the IBM FORTRAN II manual in 1967; learned C from Kernighan & Ritchie in 1980; learned Perl from my postdocs @MarkGerstein & St even Brenner in 1995; learned Excel from @john_walkenbach in 2010. Thanks🙏” / Twitter

August 27, 2021

https://twitter.com/MLevitt_NP2013/status/1431106728230326276

This is a great educational thread! I’ll bookmark it & keep in mind some of the suggestions for my bioinformatics class, which has now moved completely to python.

In particular, I’d 2nd the recommendation for this tutorial
(http://docs.python.org/tutorial), https://tutorialspoint.com/python (from @RolandDunbrack) & the O’Reilly books (from @vajkaat & @Ceaza10).

One additional thing: if you like Perl & Excel, you’ll love GAS (Google apps script, https://developers.google.com/apps-script), which provides a way to program with standard Javascript on top of Google sheets.

Reconciling modern machine-learning practice and the classical bias–variance trade-off

May 31, 2021

QT:{{“U-shaped bias–variance trade-off curve has shaped our view of model selection and directed applications of learning algorithms in practice. “}}
Nice discussion of the limitations of the bias-variance tradeoff for #DeepLearning
https://www.pnas.org/content/116/32/15849