Issue #198

April 17, 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. Neural Networks: Zero to Hero [karpathy.ai]
2. Practical Deep Learning for Coders [course.fast.ai]
3. An Introduction To Zero-Knowledge Machine Learning (ZKML) [worldcoin.org]
4. 137 emergent abilities of large language models [jasonwei.net]
5. All you need is data and functions [mckayla.blog]
6. What Are Transformer Models and How Do They Work? [txt.cohere.ai]
7. 91% of ML Models Degrade in Time [nannyml.com]

• Exposure to untrustworthy websites in the 2020 US election (R. C. Moore, R. Dahlke, J. T. Hancock)
• Disordered topological graphs enhancing nonlinear phenomena (Z. Jia, M. SeclÌ, A Avdoshkin, W. Redjem, E. Dresselhaus, J. Moore, B. Kant)
• Complex evolutionary interactions in multiple populations (K. Hu, P. Wang, J. He, M. Perc, L. Shi)
• Statistical Mechanics of Inference in Epidemic Spreading (A. Braunstein, L. Budzynski, M. Mariani)
• Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields (J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, P. Hedman)
• When do you need Chain-of-Thought Prompting for ChatGPT? (J. Chen, L. Chen, H. Huang, T. Zhou)
• Teaching Large Language Models to Self-Debug (X. Chen, M. Lin, N. Schärli, D. Zhou)

Yann LeCun and Andrew Ng: Why the 6-month AI Pause is a Bad Idea

April 17, 2023

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