Dear friends,
Welcome to the Feb 12th edition of the Sunday Briefing. We’re on hiatus from blogging, but you’re welcome to catch up on our latest Medium post “Top 10 Books we read in 2022”, the latest post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely or the latest Viz4Sci in the series: Photo Color Scheme.
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This week we’re proud to announce a brand new webinar series : Interactive Data Visualization with Python where we explore different ways in which we can build visualizations that we can interact with to better explore our data. Register now so you don’t miss out!
On our regularly scheduled content we explore a Transformative Reading List for Understanding Large Language Models, learn How to Train Really Large Models on Many GPUs and dive into the Top 10 machine learning algorithms in Finance.
On the academic front we learn about Zero-shot causal learning, networks in social systems from a mathematical modeling perspective and gain some insights into the accuracy of social scientists’ forecasts of societal change.
This week’s Data Science Book is “Practical Linear Algebra for Data Science” by M. X. Cohen. As always you can find all the previous book recommendations on our website. In the video of the week, we have “Zero-Shot Learning”
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Semper discentes,
The D4S Team
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.
