Issue #196

March 27, 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. Building a Semantic Search Engine With OpenAI and Pinecone [sigmoidprime.com]
2. Run 100B+ language models at home, BitTorrent‑style [petals.ml]
3. The genie escapes: Stanford copies the ChatGPT AI for less than $600 [newatlas.com]
4. The Unpredictable Abilities Emerging From Large AI Models [quantamagazine.org]
5. Leveraging Time-Series Segmentation and Machine Learning for Better Forecasting Accuracy [odsc.medium.com]
6. A Beginner's Guide to Synthetic Data [pub.towardsai.net]
7. Recipe for Disaster: The Formula That Killed Wall Street [wired.com]

• Strong connectivity in real directed networks (N. Rodgers, P. Tiňo, S. Johnson)
• Spatial immunization to abate disease spreading in transportation hubs (M. Mazzoli, R. Gallotti, F. Privitera, P. Colet, J. J. Ramasco)
• A manifesto for applying behavioural science (M. Hallsworth)
• Superhuman artificial intelligence can improve human decision-making by increasing novelty (M. Shin, J. Kim, B. van Opheusden, T. L. Griffiths)
• Linking social network structure and function to social preferences (J. B. Brask, A. Koher, D. P. Croft, S. Lehmann)
• Correlated Impact Dynamics in Science (J. Liu, T. Kunal, D. Wang, C. Song)
• From localized to well-mixed: How commuter interactions shape disease spread (A. Winn, A. Konkol, E. Katifori)

Let's build GPT: from scratch, in code, spelled out

March 27, 2023

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