Issue #194

March 14, 2023

This week’s Data Science Book is “ Introduction to Algorithms (4th edition) ” by T. M. Cormen, C. E. Leiderson, R. L. Rivest and C. Stein. This book is affectionately known as the bible of algorithms and over the years has proven to be an essential reading and, at over 1300 pages, a complete reference for anyone interested in gaining a broad understanding of algorithms. The content can at times be challenging but is presented in a fashion that is engaging and easily digestible. Exercises at the end of each chapter are expressly presented without the benefit of solutions but were carefully designed to help students to think algorithmically and thoroughly absorb the material presented.

Introduction to Algorithms (4th edition)

1. Demystifying bitwise operations, a gentle C tutorial [andreinc.net]
2. The next generation of AI for developers and Google Workspace [blog.google]
3. The Hidden Mathematics of Crowds: How Pedestrians Inadvertently Self-Organize [scitechdaily.com]
4. AI Chatbots Don't Care About Your Social Norms [noemamag.com]
5. How Python virtual environments work [snarky.ca]
6. Things You Can do Using Kangas Library in Data Science [heartbeat.comet.ml]
7. 13 SQL Statements for 90% of Your Data Science Tasks [levelup.gitconnected.com]
8. DataShader: Visualizing Large Datasets with Python [python.plainenglish.io]

• The dynamic nature of percolation on networks with triadic interactions (H. Sun, F. Radicchi, J. Kurths, G. Bianconi)
• Charting mobility patterns in the scientific knowledge landscape (C. K. Singh, L. Tupikina, F. Lécuyer, M. Starnini, M. Santolini)
• Opinion Dynamics on Complex Networks (J. Dong, Y.-C. Zhang, Y. Kong)
• Bayesian Graph Neural Networks: An empirical evaluation (N. Mäki)
• High-throughput Generative Inference of Large Language Models with a Single GPU (Y. Sheng, L. Zheng, B. Yuan, Z. Li, M. Ryabinin, D. Y. Fu, Z. Xie, B. Chen, C. Barrett, J.E. Gonzalez, P. Liang, C. Ré, I. Stoica, C. Zhang)
• From Books to Knowledge Graphs (N. Kokash, M. Romanello, E. Suyver, G. Colavizza)
• How to avoid machine learning pitfalls: a guide for academic researchers (M. A. Lones)

Matchings, Perfect Matchings, Maximum Matchings, and More!

March 14, 2023

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