Issue #200

May 1, 2023

This week’s Data Science Book is " Automate the Boring Stuff with Python " by A. Sweigart, an exceptional book that is perfect for anyone who wants to learn how to use Python for automating mundane tasks. The author has done an excellent job of explaining complex concepts in a simple and easy-to-understand manner. The book is well-organized and starts with the basics of Python programming, and gradually progresses to more advanced topics such as web scraping, working with Excel spreadsheets, and sending automated emails. The real-life examples and practical exercises included in each chapter make it easy for readers to apply what they have learned and see the results. One of the most remarkable aspects of this book is that it doesn't require any prior programming experience, making it perfect for beginners. However, it is also an excellent resource for experienced programmers who want to learn how to automate repetitive tasks. Overall, this is a must-read for anyone who wants to learn how to use Python for automating everyday tasks. The book is well-written, easy to follow, and the author's sense of humor makes it an enjoyable read. I highly recommend this book to anyone who wants to learn Python programming and automate their boring tasks.

Automate the Boring Stuff with Python

1. A brief history of LLaMA models [agi-sphere.com]
2. We Aren't Close To Creating A Rapidly Self-Improving AI [jacobbuckman.substack.com]
3. An introduction to lockless algorithms [lwn.net]
4. The Scientific Vision of Richard Feynman [cantorsparadise.com]
5. AI imagery goes against everything I believe photography is about [digitalcameraworld.com]
6. The Annotated Transformer [nlp.seas.harvard.edu]
7. Transformers from Scratch [e2eml.school]
8. A guide to prompting AI (for what it is worth) [oneusefulthing.org]

• Neuromorphic learning, working memory, and metaplasticity in nanowire networks (A. Loeffler, A. Diaz-Alvarez, R. Zhu, N. Ganesh, J. M. Shine, T. Nakayama, Z. Kuncic)
• Multidimensional economic complexity and inclusive green growth (V. Stojkoski, P. Koch, C. A. Hidalgo)
• Ethnic diversity fosters the social integration of refugee students (Z. Boda, G. Lorenz, M. Jansen, P. Stanat, A. Edele)
• Community Detection in Directed Weighted Networks using Voronoi Partitioning (B. Molnár, I.-B. Márton, S. Horvát, M. Ercsey-Ravasz)
• The Physics of Financial Networks (M. Bardoscia, P. Barucca, S. Battiston, F. Caccioli, G. Cimini, D. Garlaschelli, F. Saracco, T. Squartini, G. Caldarelli)
• A Cookbook of Self-Supervised Learning (R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. G. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, M. Goldblum)
• Architectures of Topological Deep Learning: A Survey on Topological Neural Networks (M. Papillon, S. Sanborn, M. Hajij, N. Miolane)

Pytorch Transformers from Scratch

May 1, 2023

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