Hi #EconTwitter! 📈 Interested in applications of #DataScience, #MachineLearning, #BigData analytics (and more) to #Economics and #Finance? 📚 If you haven't downloaded it yet, get this free book, full of accessible and inspiring contributions to this field! Link: link.springer.com/content/pd…

Hands-down the best textbook for causal inference.

RX1 Humanoid: - Opensource full human scale dual arm robot - <$1,000 - Teleoperation and pick & place objects @_buildspace @_nightsweekends

It took me 10 years to master all 24 of these statistics concepts. In the next 24 days, I'll teach them to you one by one (with examples of how I've used them in business cases). Here's what's coming: 1. Probability Distribution 2. Regression 3. Hypothesis Testing 4. Central Tendency 5. Bayesian Statistics 6. Descriptive Statistics 7. Distribution 8. Sampling 9. Confidence Interval 10. Correlation 11. Covariance 12. Dimensionality Reduction 13. Central Limit Theorem 14. Normal Distribution 15. Population and Sample 16. Skewness 17. Variance 18. Data Types 19. Probability 20. Conditional Probability 21. P Values and Level of Significance 22. Variability 23. Over and Under-Sampling 24. Estimation There you have it- my top 24 concepts on statistics that all data scientists need to know. The next problem you'll face is how to apply data science to business. I'd like to help. I’ve spent 100 hours consolidating my learnings into a free 5-day course, How to Solve Business Problems with Data Science. It comes with: 300 lines of R and Python code 5 bonus trainings 2 systematic frameworks 1 complete roadmap to avoid mistakes and start solving business problems with data science, TODAY. 👉 Here it is for free: learn.business-science.io/fr…

Matrices as Tensor network diagrams ✍️ 📷 math3ma

Large Language Models (LLMs), the engines behind the likes of Chat GPT, are capable (and incapable) of many things. But are they useful for mathematicians? @JSEllenberg has been working on LLMs with @GoogleDeepMind. Here are his thoughts. Full lecture: youtube.com/watch?v=08FGB5x0…

Are you a mathematicians? Are you interested in physics? It happened that a super team made out of Pierre Deligne, David Kazhdan, Edward Witten and more, collaborated to create this text on QFT and strings aimed at mathematicians. Deligne has a whole chapter on spinors. 👀👀

Data scientist Vlad Patryshev, at Lambdaconf, has got a great primer on model theory, where the distinction between theories and models is explained in simple terms. Short and sweet. youtube.com/watch?v=xNJHw8E_…

Statistics for Mathematicians by Victor M. Panaretos

''A bridge in between mathematicians and physicists'' This series (in 6 thick volumes) gives you every single tools you need to make your next step in your understanding of QFT and physics in general. It's literally all there. 🫡

T-halow wifi halow is being packed into the warehouse.

xv6 is amazing, my new hobby is studying everything about xv6. Awesome! The best! Now I have changed the linker script and pages, so that we really keep kernel text, read-only data, data, and bss in separate pages with the corresponding attributes. Writing access to read-only data generates an exception. Very cool, and the best part is running on KianV RV32IMA Linux xv6 RISC-V SoC.

''What every programmer should know about memory'' by Ulrich Drepper from Red Hat is still a great primer on memory utilization.

My book Interpretable Machine Learning has been career-defining. Even though the book is available on the web for free, many have supported me by buying the ebook or paperback, which ultimately helped me become a full-time writer. I'm grateful for all your support 🙏

On my list.

Hi #EconTwitter! 📈 Are you into #econometrics and data analysis? And looking for an amazing set of slides on the topic? Don't miss 👇 @GaborBekes's collection of slides from his book on data analysis. 📚 Very well-crafted and clear, they cover a lot of material. Cool stuff! ⭐️ Links Slides: gabors-data-analysis.com/cha… Code etc: github.com/gabors-data-analy…