Issue #255
September 11, 2024
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.
- 1. Timeseries Indexing at Scale [artem.krylysov.com]
- 2. Why Physics Is Unreasonably Good at Creating New Math [nautil.us]
- 3. Building LLMs from the Ground Up: A 3-hour Coding Workshop [magazine.sebastianraschka.com]
- 4. How We Made Jupyter Notebooks Load 10 Times Faster [singlestore.com]
- 5. Web Security Basics (with htmx) [htmx.org]
- 6. Lesser known parts of Python standard library [trickster.dev]
- 7. Why I'm lukewarm on graph neural networks [singlelunch.com]
- • Why do large language models hallucinate? (J. Waldo, S. Boussard)
- • 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)
- • Tutorial on Diffusion Models for Imaging and Vision (S. H. Chan)
- • Operational Advice for Dense and Sparse Retrievers: HNSW, Flat, or Inverted Indexes? (J. Lin)
- • Manipulating Large Language Models to Increase Product Visibility (A. Kumar, H. Lakkaraju)
- • 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 our videos are also available in our YouTube playlist.
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