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 👇 Gábor Békés's collection of slides from his book on data analysis. 📚 Very well-crafted and clear, they cover a lot of material. Cool stuff!

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

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

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. 🫡

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=xNJHw8…

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? Jordan Ellenberg has been working on LLMs with Google DeepMind. Here are his thoughts. Full lecture: youtube.com/watch?v=08FGB5…

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. 👀👀

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

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 nights & weekends

Matrices as Tensor network diagrams ✍️ 📷 math3ma

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: