Issue #198
April 17, 2023
Dear friends,
Welcome to the 198th edition of the Sunday Briefing. This week we’re on hiatus from blogging. While we work on our next post, you can catch up on the latest post in the Viz4Sci series: Waterfall Chart. The latest Medium post “Top 10 Books we read in 2022” and the most recent post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely.
Several of you have reached out in recent weeks asking how they can help support the work we do here at Data For Science. While the Sunday Briefing is a labor of love and will always remain free, it is not without costs, and any help is appreciated.
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The very first edition of the fully revamped Probability and Statistics For Everyone is coming up on Apr 25th. This will be your chance to get up to speed with the fundamentals of Probability and Statistics from the ground up. There’s still some slots open, so Register now so you don’t miss out!
We’re also proud to announce the first edition of the brand new Graphs for Data Science webinar series coming up on May 10th. Registrations have just opened and you can be one of the first ones to Sign Up!
On our regularly scheduled content, we have An Introduction To Zero-Knowledge Machine Learning (ZKML), a course on Neural Networks: Zero to Hero and survey of 137 emergent abilities of large language models.
On the academic front, we explore the Statistical Mechanics of Inference in Epidemic Spreading, how we can Teach Large Language Models to Self-Debug and how Disordered topological graphs enhance nonlinear phenomena.
This week’s Data Science Book is “The Recursive Book of Recursion” by Al Sweigart. As always you can find all the previous book recommendations on our website. In the video of the week, we have the perspective of Yann LeCun and Andrew Ng on Why the 6-month AI Pause is a Bad Idea.
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Semper discentes,
The D4S Team
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.
- 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
All our videos are also available in our
YouTube playlist.
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