Issue #199

April 24, 2023

This week’s Data Science Book is " The Recursive Book of Recursion " by Al Sweigart, a highly recommended book for programmers of all levels. The book explains recursion in a clear and approachable way. It begins by laying important groundwork and explains functions and their operation and features. The author spends significant time explaining the call stack, what it does, how it is structured, and how it operates, leading to a discussion of ‘stack overflow,’ one of the risks of using recursion. He then devotes an entire chapter to comparing recursion and iteration, demonstrating that in the vast majority of cases, recursive functions are not necessary and in some cases perform worse than their iterative counterparts. However, the book also shows where recursion is actually a good idea and where it is a good fit. Sweigart explores traversing tree structures and demonstrates how memoization can improve the efficiency of some recursive functions. The book ends with several projects that build on the concepts that come before, including the Droste Effect, a recursive art technique that generates a similar recursive image from any photograph or drawing utilizing images. Overall, the Recursive Book of Recursion is a great read for beginners and intermediate programmers alike. The book teaches about recursion and stretches the reader to think differently while confidently showing that seemingly lofty concepts are within reach.

The Recursive Book of Recursion

1. What can ChatGPT teach us about economics? [weforum.org]
2. Prompt Engineering vs. Blind Prompting [mitchellh.com]
3. Finetuning Large Language Models [magazine.sebastianraschka.com]
4. Batch computing and the coming age of AI systems [hazyresearch.stanford.edu]
5. The Modern Transactional Stack [a16z.com]
6. Transformer Math 101 [blog.eleuther.ai]
7. Im2Vec: Synthesizing Vector Graphics without Vector Supervision [geometry.cs.ucl.ac.uk]
8. Large Language Models: Scaling Laws and Emergent Properties [cthiriet.com]

• Emergent stability in complex network dynamics (C. Meena, C. Hens, S. Acharyya, S. Haber, S. Boccaletti, B. Barzel)
• Relation between the degree and betweenness centrality distribution in complex networks (H. Masoomy, V. Adami, M. N. Najafi)
• The Blockchain Imitation Game (K. Qin, S. Chaliasos, L. Zhou, B. Livshits, D. Song, A. Gervais)
• The Unreasonable Effectiveness of Contact Tracing on Networks with Cliques (A. K. Rizi, L. A. Keating, J. P. Gleeson, D. J. P. O'Sullivan, M. Kivelä)
• The law of activity delays (A. Vazquez, C. Marasinou, G. Kalogridis, C. Ellinas)
• Deep Learning Criminal Networks (H. V. Ribeiro, D. D. Lopes, A. A. B. Pessa, A. F. Martins, B. R. da Cunha, S. Goncalves, E. K. Lenzi, Q. S. Hanley, M. Perc)
• Models used to characterize blockchain features. A systematic literature review and bibliometric analysis (J. J. Rico-Peña, R. Arguedas-Sanz, C. López-Martin)

Beginners Guide to GPT4 API & ChatGPT 3.5 Turbo API Tutorial

April 24, 2023

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