Issue #190

February 12, 2023

This week’s Data Science Book is " Practical Linear Algebra for Data Science " by M. X. Cohen, a book written for self-studying learners who need to learn how to apply linear algebra in their work. The book is self-contained and can be used as a standalone resource, but it can also be used as a supplement to a lecture-based course. Whether you are trying to enhance your understanding of linear algebra or learn the subject from scratch, this is a valuable resource that provides a clear and practical approach to the subject. The author is an excellent instructor that recognizes that traditional linear algebra textbooks can be frustrating for those looking to use the subject as a tool for understanding data, statistics, deep learning, image processing, and other technical fields. Instead of memorizing equations and abstract proofs, the author provides clear explanations and practical examples to help the reader understand how to think about matrices, vectors, and operations. The focus of the book is to help the reader develop a visual, geometric, intuition for linear algebra and how to implement these concepts in Python code, particularly for applications in machine learning and data science.

Practical Linear Algebra for Data Science

1. Understanding Large Language Models -- A Transformative Reading List [sebastianraschka.com]
2. How to Train Really Large Models on Many GPUs? [lilianweng.github.io]
3. The Inference Cost Of Search Disruption – Large Language Model Cost Analysis [semianalysis.com]
4. Computer Graphics and Computer Animation: A Retrospective Overview [ohiostate.pressbooks.pub]
5. Donald Knuth on Machine Learning and the Meaning of Life [thenewstack.io]
6. QGIS is the mapping software you didn't know you needed [chollinger.com]
7. Top 10 machine learning algorithms in Finance [datadriveninvestor.com]

• Insights into the accuracy of social scientists’ forecasts of societal change (The Forecasting Collaborative)
• Using cognitive psychology to understand GPT-3 (M. Binz, E. Schulz)
• Automating Terror: The Role and Impact of Telegram Bots in the Islamic State’s Online Ecosystem (A. Alrhmoun, C. Winter, J. Kertész)
• Theory of Mind May Have Spontaneously Emerged in Large Language Models (M. Kosinski)
• What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 (A. Shevtsov, M. Oikonomidou, D. Antonakaki, P. Pratikakis, S. Ioannidis)
• Zero-shot causal learning (H. Nilforoshan, M. Moor, Y. Roohani, Y. Chen, A. Šurina, M. Yasunaga, S. Oblak, J. Leskovec)
• A tutorial on networks in social systems: A mathematical modeling perspective (H. Z. Brooks)

Zero-Shot Learning

February 12, 2023

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