Issue #214
August 16, 2023
This week’s Data Science Book, "Learning Git", by Anna Skoulikari. This is a remarkable book that caters to both technical and non-technical individuals seeking to master Git. The book's rainbow project approach offers an effective and enjoyable way to understand the inner workings of Git, covering all the basics needed for practical use in an industry setting. From setting up local repositories to managing remote ones, the book excels in simplifying complex concepts with colorful diagrams and highlighted keywords. Highly recommended for anyone looking to grasp Git's fundamentals and achieve confident control over version control in their work.
- 1. Risky Giant Steps Can Solve Optimization Problems Faster [quantamagazine.org]
- 2. Quantum AI breakthrough: theorem shrinks appetite for training data [discover.lanl.gov]
- 3. A gentle introduction to information geometry [robots.ox.ac.uk]
- 4. Beginner’s guide to Llama models [agi-sphere.com]
- 5. Do Machine Learning Models Memorize or Generalize? [pair.withgoogle.com]
- 6. Learning Algorithms [paedubucher.ch]
- 7. The Two Metrics That Reveal True Data Dispersion Beyond Standard Deviation [towardsdatascience.com]
- • Vitamin interdependencies predicted by metagenomics-informed network analyses and validated in microbial community microcosms (T. Hessler, R. J. Huddy, R. Sachdeva, S. Lei, S. T. L. Harrison, S. Diamond, J. F. Banfield)
- • Effects of COVID-19 vaccination and previous infection on Omicron SARS-CoV-2 infection and relation with serology (B. de Gier, A. J. Huiberts, C. E. Hoeve, G. den Hartog, H. van Werkhoven, R. van Binnendijk, S. J. M. Hahné, H. E. de Melker, S. van den Hof, M. J. Knol)
- • An introduction to graph theory (D. Grinberg)
- • Machine Learning for Infectious Disease Risk Prediction: A Survey (M. Liu, Y. Liu, J. Liu)
- • MetaGPT: Meta Programming for Multi-Agent Collaborative Framework (S. Hong, X. Zheng, J. Chen, Y. Cheng, J. Wang, C. Zhang, Z. Wang, S. K. S. Yau, Z. Lin, L. Zhou, C. Ran, L. Xiao, C. Wu)
- • Predicting Information Pathways Across Online Communities (Y. Jin, Y.-C. Lee, K. Sharma, M. Ye, K. Sikka, A. Divakaran, S. Kumar)
- • Thermodynamic Linear Algebra (M. Aifer, K. Donatella, M. H. Gordon, T. Ahle, D. Simpson, G. E. Crooks, P. J. Coles)
Professional Preprocessing with Pipelines in Python
All our videos are also available in our YouTube playlist.
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