Issue #210
July 19, 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. Large Multiplication [numberworld.org]
- 2. Introduction to Vector Similarity Search [zilliz.com]
- 3. Computation and State Machines [lamport.azurewebsites.net]
- 4. Faster Neural Networks Straight from JPEG [uber.com]
- 5. The risks of AI are real but manageable [gatesnotes.com]
- 6. A Petabyte of Health Insurance Prices Per Month [blog.turquoise.health]
- 7. How much information is too much information? [sourcery.ai]
- • Descriptive vs. Inferential Community Detection in Networks (T. P. Peixoto)
- • How do we know how smart AI systems are? (M. Mitchell)
- • From second thoughts on the germ theory to a full-blown host theory (J.-L. Casanova)
- • Long ties, disruptive life events, and economic prosperity (E. Jahani, S. P. Fraiberger, M. Bailey, D. Eckles)
- • Financial Machine Learning (B. T. Kelly, D. Xiu)
- • Monte Carlo samplers for efficient network inference (Z. Kilic, M. Schweiger, C. Moyer, S. Pressé)
- • Temporal network compression via network hashing (R. Vaudaine, P. Borgnat, P. Goncalves, R. Gribonval, M. Karsai)
Causal Inference - Explained!
All our videos are also available in our YouTube playlist.
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