Welcome to the Sept 11th edition of the Data Science Briefing!
We want to remind you that the very first edition of the LLMs for Data Science webinar will be held in just over a week on September 19. There are still a few spots left, so Register now so you don't miss out!
We're proud to announce the next edition of the ChatGPT, and Competing LLMs webinar has just been confirmed for November 13, and you can already reserve your spot!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we explore Timeseries Indexing at Scale, Why Physics Is Unreasonably Good at Creating New Math, andWeb Security Basics (with htmx).
On the academic front, we learn why large language models hallucinate, review Graph Language Models, Graph Neural Networks in Epidemic Modeling and dive into a Tutorial on Diffusion Models for Imaging and Vision.
This week's book recommendation is "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. You can find all the previous book recommendations on our website. In this week's video, we have Eric Siegel explaining why Generative AI is not the panacea we’ve been promised.
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, go ahead and forward this email to them. This will help us spread the word!
Semper discentes,
The D4S Team
This week's book is "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. Networks are the keystone concept necessary to understand a wealth of real-world complex systems whose behavior is characterized by interactions between individual components. In this book, J. Bagrow and Y.-Y. Ahn, two leading researchers in the field of Complex Networks, introduce readers to the fundamental concepts of Network Science and how to apply them to practical datasets. Their hands-on approach will get you up to speed quickly, allowing you to develop effective approaches to understanding your own network datasets.
- Timeseries Indexing at Scale [artem.krylysov.com]
- Why Physics Is Unreasonably Good at Creating New Math [nautil.us]
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Building LLMs from the Ground Up: A 3-hour Coding Workshop [magazine.sebastianraschka.com]
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How We Made Jupyter Notebooks Load 10 Times Faster [singlestore.com]
- Web Security Basics (with htmx) [htmx.org]
- Lesser known parts of Python standard library [trickster.dev]
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Why I'm lukewarm on graph neural networks [singlelunch.com]
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Why do large language models hallucinate? (J. Waldo, S. Boussard)
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The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers (Z. Cui, S. Jaffe, S. Peng, T. Salz)
- Graph Language Models (M. Plenz, A. Frank)
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Tutorial on Diffusion Models for Imaging and Vision (S. H. Chan)
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Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? (J. Lin)
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Manipulating Large Language Models to Increase Product Visibility (A. Kumar, H. Lakkaraju)
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A Review of Graph Neural Networks in Epidemic Modeling (Z. Liu, G. Wan, B. A. Prakash, M. S. Y. Lau, W. Jin)
Generative AI is not the panacea we’ve been promised
All the videos of the week are now available in our Youtube playlist.
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