Issue #203
May 29, 2023
This week’s Data Science Book is " Fluent Python " by L. Ramalho, which is, in my opinion, the best book on Python programming available as it teaches readers to truly understand how Python works and how to utilize it effectively. This is a book for those who want a comprehensive and in-depth understanding of the language, covering advanced topics such as metaprogramming with "dunder()" methods without getting lost in the weeds. It covers exactly what you need to know, without overwhelming you with unnecessary information. "Fluent Python" is a must-have for any serious Python programmer as even after almost twenty years of working with Python, I continue to learn new things with each chapter. The second edition weighs in at over 1,000 pages for an in depth, comprehensive cover, of everything you may ever need to know about Python.
- 1. Tracing Python [blog.koehntopp.info]
- 2. A tutorial on Principal Components Analysis [cs.otago.ac.nz]
- 3. Our model suggests that global deaths remain 5% above pre-covid forecasts [economist.com]
- 4. Finetuning LLMs Efficiently with Adapters [magazine.sebastianraschka.com]
- 5. How to Access the Fantasy Premier League API, Build a Dataframe, and Analyze Using Jupyter, Python, and Pandas [towardsdatascience.com]
- 6. More Than Just Algorithms [queue.acm.org]
- 7. Deep Neural Networks As Computational Graphs [medium.com/tebs-lab]
- • Causal evidence that herpes zoster vaccination prevents a proportion of dementia cases (M. Eyting, M. Xie, S. Heß, S. Heß, P. Geldsetzer)
- • A positive statistical benchmark to assess network agreement (B. Hao, I. A. Kovács)
- • Hierarchical community structure in network
- • Epidemic control in networks with cliques (L. D. Valdez, L. Vassallo, L. A. Braunstein)
- • A PhD Student's Perspective on Research in NLP in the Era of Very Large Language Models (O. Ignat, Z. Jin, A. Abzaliev, L. Biester, S. Castro, N. Deng, X. Gao, A. Gunal, J. He, A. Kazemi, et al)
- • Urban Dynamics Through the Lens of Human Mobility (Y. Xu, L. E. Olmos, D. Mateo, A. Hernando, X. Yang, M. C. Gonzalez)
- • Assortative and preferential attachment lead to core-periphery networks (J. Ureña-Carrion, F. Karimi, G. Iñiguez, M. Kivelä)
Network Science: From Abstract to Physical Networks
All our videos are also available in our YouTube playlist.
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