A few of the books/blog posts with the highest return on invested time that I have come across. I will update this list as time goes by. Enjoy.
A breakthrough paper in economics. Hayek provides unshakable arguments in favor of the efficiency of market economies as opposed to centrally planned economies. Fundamental ideas such as price signals and spontaneous order are explained.
Not enough people know about this phenomenon and I this is the best article I found so far to explain it. In a nutshell, the process of augmentation of the money supply tends to benefit the people closest to the source of the new money. In the 21st century, this means banks and financiers.
It’s crazy how people don’t realize how poverty, diseases and illiteracy have receded in the last two hundred years. Technology has enabled us to live in the most prosperous era of human history and Matt Ridley does a great job of educating the reader on this. Will we be able to make this prosperity last ?
Everyone should read this book. The a priori assumption that almost all phenomena follow Gaussian curves plagues a plethora of disciplines, and Taleb does a great job of vulgarization of this problem, initially stated by the late Benoit Mandelbrot.
This serial entrepreneur Paul Graham’s guide to startups. You can tell a lot of experience and a lot of very careful thinking went into this essay.
Originally a thread of tweets, you can now read this series of ideas of how to create wealth on thread reader. Every single tweet is eye opening, but the idea that stands out to me is to learn how to detect non-linear payoffs.
Very entertaining book by Taleb with a lot of signal and very little noise. People sometimes criticize Taleb’s writing style, saying he doesn’t get to the point and keeps beating around the bush discussing all sorts of disciplines. To me, it’s precisely this flanneur style of writing, with the anecdotes and historical examples, that I find appealing. Very important ideas also, such as optionality, non-linear rewards, hormesis, iatrogenesis etc…
An introduction to the Hayekian-Popperian philosophy of science. Popper debunks historicist doctrines, which consider that we can discover the laws which govern human history in order to make predictions about the future of our society. Among these doctrines we can find Marxism, Hegelianism or more recently Pikettism ! This is a difficult book, but a good explanation of its central tenet can be found here.
Best introduction you will find to the field of philosophy of science, period. It covers deduction/induction, the black swan problem, realism/anti-realism, inference to the best explanation and more.
A review of both the scientific literature and ancient philosophy on happiness. Haidt draws parallels between the two and finds that often, the ancients were pretty much spot on ! A great tool to engineer a happy life.
In this book James Clear explains his evidence based methodology to build productive habits. The book nonetheless does not read like a catalog of research findings and is very pragmatically oriented. 90% of self help books say the same things, but this one actually brings added value to the genre.
I won’t write a review for this essay. Just read it, you’ll thank me later.
Machine learning (beginners)
Best introduction to machine learning I have come across. The explanations are clear and you get your hands dirty actually coding machine learning systems. Some chapters are a bit hard to understand if you don’t know the math behind ML. I would recommend starting with this book and consulting the following one whenever you want to better understand the math behind a ML algorithm.
I am currently going trough this book. I had been looking for a resource to learn/review the math behind ML, all I could find were separate courses on calculus, linear algebra etc… This books puts everything in one place and the explanations are very clear. Note that you don’t necessarily need to understand the math behind ML to do ML. But if you want to implement state of the art papers, you do.
A github repo with all the breakthrough papers in the history of deep learning. I have not read them all yet but I am on my way !